Colloquium
We usually meet (with a few exceptions, please see below) on Fridays at 10:00 am (Eastern Time). If you are interested in joining, please fill out the Registration Form. For questions please contact Harbir Antil (hantil@gmu.edu).
Upcoming Events
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title  
Friday, January 28, 2022 
Felix Otto  MaxPlanckInstitut für Mathematik in den Naturwissenschaften  Regularity theory for optimal transportation and an application to the matching problem ... more ... less  
Fri, Jan 28, 2022 
Felix Otto, MaxPlanckInstitut für Mathematik in den Naturwissenschaften Regularity theory for optimal transportation and an application to the matching problem ... more ... less 

Abstract: The optimal transportation of one measure into another, leading to the notion of their Wasserstein distance, is a problem in the calculus of variations with a wide range of applications. The subtle regularity theory for the optimal map is traditionally based on the regularity theory for the MongeAmpere equation, which was revolutionized by Caffarelli, based on comparison principle arguments. We present a purely variational approach to the regularity theory for optimal transportation, introduced with M.~Goldman and refined with M.~Huesmann. Following De Giorgi's philosophy for the regularity theory of minimal surfaces, it is based on the approximation of the displacement by a harmonic gradient, through the construction of a variational competitor. Due to its robustness and lowregularity approach, this approach is like taylormade to study the popular problem of matching two independent Poisson point processes. For example, it can be used to prove nonexistence of a stationary cyclically monotone coupling, which is joint work with M.~Huesmann and F.~Mattesini. 

Abstract: The optimal transportation of one measure into another, leading to the notion of their Wasserstein distance, is a problem in the calculus of variations with a wide range of applications. The subtle regularity theory for the optimal map is traditionally based on the regularity theory for the MongeAmpere equation, which was revolutionized by Caffarelli, based on comparison principle arguments. We present a purely variational approach to the regularity theory for optimal transportation, introduced with M.~Goldman and refined with M.~Huesmann. Following De Giorgi's philosophy for the regularity theory of minimal surfaces, it is based on the approximation of the displacement by a harmonic gradient, through the construction of a variational competitor. Due to its robustness and lowregularity approach, this approach is like taylormade to study the popular problem of matching two independent Poisson point processes. For example, it can be used to prove nonexistence of a stationary cyclically monotone coupling, which is joint work with M.~Huesmann and F.~Mattesini. 

Friday, February 04, 2022 
Barbara Kaltenbacher  University of Klagenfurt  Reduced, allatonce, and variational formulations of inverse problems and their iterative solution ... more ... less  
Fri, Feb 04, 2022 
Barbara Kaltenbacher , University of Klagenfurt Reduced, allatonce, and variational formulations of inverse problems and their iterative solution ... more ... less 

Abstract: The conventional way of formulating inverse problems such as identification of a (possibly infinite dimensional) parameter, is via some forward operator, which is the concatenation of the observation operator with the parametertostatemap for the underlying model. Recently, allatonce formulations have been considered as an alternative to this reduced formulation, avoiding the use of a parametertostate map, which would sometimes lead to too restrictive conditions. Here the model and the observation are considered simultaneously as one large system with the state and the parameter as unknowns. A still more general formulation of inverse problems, containing both the reduced and the allatonce formulation, but also the wellknown and highly versatile socalled variational approach (not to be mistaken with variational regularization) as special cases, is to formulate the inverse problem as a minimization problem (instead of an equation) for the state and parameter. Regularization can be incorporated via imposing constraints and/or adding regularization terms to the objective. We will consider iterative regularization methods resulting from the application of gradient or Newton type iterations to such minimization based formulations and provide convergence results. In doing so, instead of regularizing the minimization problem and then applying standard iterative optimization methods, we regularize *by* iterating, more precisely by early stopping. 

Abstract: The conventional way of formulating inverse problems such as identification of a (possibly infinite dimensional) parameter, is via some forward operator, which is the concatenation of the observation operator with the parametertostatemap for the underlying model. Recently, allatonce formulations have been considered as an alternative to this reduced formulation, avoiding the use of a parametertostate map, which would sometimes lead to too restrictive conditions. Here the model and the observation are considered simultaneously as one large system with the state and the parameter as unknowns. A still more general formulation of inverse problems, containing both the reduced and the allatonce formulation, but also the wellknown and highly versatile socalled variational approach (not to be mistaken with variational regularization) as special cases, is to formulate the inverse problem as a minimization problem (instead of an equation) for the state and parameter. Regularization can be incorporated via imposing constraints and/or adding regularization terms to the objective. We will consider iterative regularization methods resulting from the application of gradient or Newton type iterations to such minimization based formulations and provide convergence results. In doing so, instead of regularizing the minimization problem and then applying standard iterative optimization methods, we regularize *by* iterating, more precisely by early stopping. 

Friday, February 11, 2022 

Fri, Feb 11, 2022 


Friday, February 18, 2022 
Daniel Wachsmuth  Julius Maximilians University of Würzburg  Proximal gradient methods for control problems with nonsmooth and nonconvex control cost ... more ... less  
Fri, Feb 18, 2022 
Daniel Wachsmuth, Julius Maximilians University of Würzburg Proximal gradient methods for control problems with nonsmooth and nonconvex control cost ... more ... less 

Abstract: We investigate the convergence of the proximal gradient method applied to control problems with nonsmooth and nonconvex control cost. Here, we focus on control cost functionals that promote sparsity, which includes functionals of L^ptype for p in [0, 1). We prove stationarity properties of weak limit points of the method. These properties are weaker than those provided by Pontryagin’s maximum principle and weaker than Lstationarity. 

Abstract: We investigate the convergence of the proximal gradient method applied to control problems with nonsmooth and nonconvex control cost. Here, we focus on control cost functionals that promote sparsity, which includes functionals of L^ptype for p in [0, 1). We prove stationarity properties of weak limit points of the method. These properties are weaker than those provided by Pontryagin’s maximum principle and weaker than Lstationarity. 

Friday, February 25, 2022 
Prasanna Balaprakash  Argonne National Laboratory  Automated Machine Learning with DeepHyper ... more ... less  
Fri, Feb 25, 2022 
Prasanna Balaprakash, Argonne National Laboratory Automated Machine Learning with DeepHyper ... more ... less 

Abstract: Scientific data sets are diverse and often require datasetspecific deep neural network (DNN) models. Nevertheless, designing highperforming DNN architecture for a given data set is an expertdriven, timeconsuming, trialanderror manual task. To that end, we have developed DeepHyper [1], a software package that uses scalable neural architecture and hyperparameter search to automate the design and development of DNN models for scientific and engineering applications. In this talk, we will focus on two new algorithmic components that we developed recently. The first is DeepHyper/AgEBO [2] that seeks to reduce the overall computation time by combining Aging Evolution (AE) to search over neural architectures and asynchronous Bayesian optimization (BO) to tune hyperparameters of dataparallel training. The second is DeepHyper/AutoDEUQ [3], an automated approach for generating an ensemble of deep neural networks and using them for estimating aleatoric (data) and epistemic (model) uncertainties. [1] https://deephyper.readthedocs.io/en/latest/ [2] R. Egele, P. Balaprakash, I. Guyon, V. Vishwanath, F. Xia, R. Stevens, Z. Liu. AgEBOTabular: Joint neural architecture and hyperparameter search with autotuned dataparallel training for tabular data. In SC21: International Conference for High Performance Computing, Networking, Storage and Analysis, 2021. [3] R. Egele, R. Maulik, K. Raghavan, P. Balaprakash, B. Lusch. AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification, (in review), 2021. 

Abstract: Scientific data sets are diverse and often require datasetspecific deep neural network (DNN) models. Nevertheless, designing highperforming DNN architecture for a given data set is an expertdriven, timeconsuming, trialanderror manual task. To that end, we have developed DeepHyper [1], a software package that uses scalable neural architecture and hyperparameter search to automate the design and development of DNN models for scientific and engineering applications. In this talk, we will focus on two new algorithmic components that we developed recently. The first is DeepHyper/AgEBO [2] that seeks to reduce the overall computation time by combining Aging Evolution (AE) to search over neural architectures and asynchronous Bayesian optimization (BO) to tune hyperparameters of dataparallel training. The second is DeepHyper/AutoDEUQ [3], an automated approach for generating an ensemble of deep neural networks and using them for estimating aleatoric (data) and epistemic (model) uncertainties. [1] https://deephyper.readthedocs.io/en/latest/ [2] R. Egele, P. Balaprakash, I. Guyon, V. Vishwanath, F. Xia, R. Stevens, Z. Liu. AgEBOTabular: Joint neural architecture and hyperparameter search with autotuned dataparallel training for tabular data. In SC21: International Conference for High Performance Computing, Networking, Storage and Analysis, 2021. [3] R. Egele, R. Maulik, K. Raghavan, P. Balaprakash, B. Lusch. AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification, (in review), 2021. 

Friday, March 04, 2022 
Richard J. Braun  University of Delaware  
Fri, Mar 04, 2022 
Richard J. Braun, University of Delaware


Friday, March 11, 2022 
Mark Embree  Virginia Tech  
Fri, Mar 11, 2022 
Mark Embree, Virginia Tech


Friday, March 18, 2022 
no colloquium  
Fri, Mar 18, 2022 
no colloquium  
Friday, March 25, 2022 
Steven Rodriguez  Naval Research Lab  Enabling Rapid Meshless Multiphysics Modeling with Hybrid DataDriven Projection Tree ReducedOrder Modeling ... more ... less  
Fri, Mar 25, 2022 
Steven Rodriguez, Naval Research Lab Enabling Rapid Meshless Multiphysics Modeling with Hybrid DataDriven Projection Tree ReducedOrder Modeling ... more ... less 

Abstract: 

Abstract: 

Friday, April 01, 2022 
East Cost Optimization Meeting (ECOM)  George Mason University  
Fri, Apr 01, 2022 
East Cost Optimization Meeting (ECOM), George Mason University


Friday, April 08, 2022 
no colloquium  
Fri, Apr 08, 2022 
no colloquium  
Friday, April 15, 2022 

Fri, Apr 15, 2022 


Friday, April 22, 2022 
Bethany Lusch  Argonne National Laboratory  
Fri, Apr 22, 2022 
Bethany Lusch, Argonne National Laboratory


Friday, April 29, 2022 

Fri, Apr 29, 2022 

Previous Events
Spring 2022 ... hide
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title 
Fall 2021 ... hide
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title  
Friday, August 27, 2021 
Howard Elman  University of Maryland College Park  Surrogate Approximation of the GradShafranov Free Boundary Problem via Stochastic Collocation on Sparse Grids ... more ... less  
Fri, Aug 27, 2021 
Howard Elman, University of Maryland College Park Surrogate Approximation of the GradShafranov Free Boundary Problem via Stochastic Collocation on Sparse Grids ... more ... less 

Abstract: In magnetic confinement fusion devices, the equilibrium configuration of a plasma is determined by the balance between the hydrostatic pressure in the fluid and the magnetic forces generated by an array of external coils and the plasma itself. The location of the plasma is not known a priori and must be obtained as the solution to a free boundary problem. The partial differential equation that determines the behavior of the combined magnetic field depends on a set of physical parameters (location of the coils, intensity of the electric currents going through them, magnetic permeability, etc.) that are subject to uncertainty and variability. The confinement region is in turn a function of these stochastic parameters as well. In this work, we consider variations on the current intensities running through the external coils as the dominant source of uncertainty. This leads to a parameter space of dimension equal to the number of coils in the reactor. With the aid of a surrogate function built on a sparse grid in parameter space, a Monte Carlo strategy is used to explore the effect that stochasticity in the parameters has on important features of the plasma boundary. The use of the surrogate function reduces the time required for the Monte Carlo simulations by factors that range between 7 and over 30. Joint work with Jiaxing Liang (Applied Mathematics Program, University of Maryland) and Tonatiuh SánchezVizuet (Department of Mathematics, University of Arizona). 

Abstract: In magnetic confinement fusion devices, the equilibrium configuration of a plasma is determined by the balance between the hydrostatic pressure in the fluid and the magnetic forces generated by an array of external coils and the plasma itself. The location of the plasma is not known a priori and must be obtained as the solution to a free boundary problem. The partial differential equation that determines the behavior of the combined magnetic field depends on a set of physical parameters (location of the coils, intensity of the electric currents going through them, magnetic permeability, etc.) that are subject to uncertainty and variability. The confinement region is in turn a function of these stochastic parameters as well. In this work, we consider variations on the current intensities running through the external coils as the dominant source of uncertainty. This leads to a parameter space of dimension equal to the number of coils in the reactor. With the aid of a surrogate function built on a sparse grid in parameter space, a Monte Carlo strategy is used to explore the effect that stochasticity in the parameters has on important features of the plasma boundary. The use of the surrogate function reduces the time required for the Monte Carlo simulations by factors that range between 7 and over 30. Joint work with Jiaxing Liang (Applied Mathematics Program, University of Maryland) and Tonatiuh SánchezVizuet (Department of Mathematics, University of Arizona). 

Friday, September 03, 2021 
Meenakshi Singh  Colorado School of Mines  Investigating quantum speed limits with superconducting qubits ... more ... less  
Fri, Sep 03, 2021 
Meenakshi Singh, Colorado School of Mines Investigating quantum speed limits with superconducting qubits ... more ... less 

Abstract: The speed at which quantum entanglement between qubits with short range interactions can be generated is limited by the LiebRobinson bound. Introducing longer range interactions relaxes this bound and entanglement can be generated at a faster rate. The speed limit for this has been analytically found only for a twoqubit system under the assumption of negligible single qubit gate time. We seek to demonstrate this speed limit experimentally using two superconducting transmon qubits. Moreover, we aim to measure the increase in this speed limit induced by introducing additional qubits (coupled with the first two). Since the speed up grows with additional entangled qubits, it is expected to increase as the system size increases. This has important implications for largescale quantum computing. Bio:Dr. Singh is an experimental physicist with research focused on quantum thermal effects and quantum computing. She graduated from the Indian Institute of Technology with an M. S. in Physics in 2006 and received a Ph. D. in Physics from the Pennsylvania State University in 2012. Her Ph. D. thesis was focused on quantum transport in nanowires. She went on to work at Sandia National Laboratories on Quantum Computing as a postdoctoral scholar. Since 2017, she is an Assistant Professor in the Department of Physics at the Colorado School of Mines. At Mines, her research projects include measurements of entanglement propagation and thermal effects in superconducting hybrids. She recently received the NSF CAREER award to pursue research in phonon interactions with spin qubits in silicon quantum dots. 

Abstract: The speed at which quantum entanglement between qubits with short range interactions can be generated is limited by the LiebRobinson bound. Introducing longer range interactions relaxes this bound and entanglement can be generated at a faster rate. The speed limit for this has been analytically found only for a twoqubit system under the assumption of negligible single qubit gate time. We seek to demonstrate this speed limit experimentally using two superconducting transmon qubits. Moreover, we aim to measure the increase in this speed limit induced by introducing additional qubits (coupled with the first two). Since the speed up grows with additional entangled qubits, it is expected to increase as the system size increases. This has important implications for largescale quantum computing. Bio:Dr. Singh is an experimental physicist with research focused on quantum thermal effects and quantum computing. She graduated from the Indian Institute of Technology with an M. S. in Physics in 2006 and received a Ph. D. in Physics from the Pennsylvania State University in 2012. Her Ph. D. thesis was focused on quantum transport in nanowires. She went on to work at Sandia National Laboratories on Quantum Computing as a postdoctoral scholar. Since 2017, she is an Assistant Professor in the Department of Physics at the Colorado School of Mines. At Mines, her research projects include measurements of entanglement propagation and thermal effects in superconducting hybrids. She recently received the NSF CAREER award to pursue research in phonon interactions with spin qubits in silicon quantum dots. 

Friday, September 10, 2021 
MinhBinh Tran  Southern Methodist University  On the wave turbulence theory for stochastic and random multidimensional KdV type equations ... more ... less  
Fri, Sep 10, 2021 
MinhBinh Tran, Southern Methodist University On the wave turbulence theory for stochastic and random multidimensional KdV type equations ... more ... less 

Abstract: In this talk, I consider a multidimensional KdV type equation, the ZakharovKuznetsov (ZK) equation. I will present a derivation of the 3wave kinetic equation from both the stochastic ZK equation and the deterministic ZK equation with random initial condition. The equation is given on a hypercubic lattice of size . In the case of the stochastic ZK equation, I will show that the two point correlation function can be asymptotically expressed as the solution of the 3wave kinetic equation at the kinetic limit under very general assumptions, in which the initial condition is out of equilibrium and the size of the domain is fixed. In the case of the deterministic ZK equation with random initial condition, the kinetic equation can also be derived at the kinetic limit, but under more restrictive assumptions. This is a joint work with Gigliola Staffilani (MIT). 

Abstract: In this talk, I consider a multidimensional KdV type equation, the ZakharovKuznetsov (ZK) equation. I will present a derivation of the 3wave kinetic equation from both the stochastic ZK equation and the deterministic ZK equation with random initial condition. The equation is given on a hypercubic lattice of size . In the case of the stochastic ZK equation, I will show that the two point correlation function can be asymptotically expressed as the solution of the 3wave kinetic equation at the kinetic limit under very general assumptions, in which the initial condition is out of equilibrium and the size of the domain is fixed. In the case of the deterministic ZK equation with random initial condition, the kinetic equation can also be derived at the kinetic limit, but under more restrictive assumptions. This is a joint work with Gigliola Staffilani (MIT). 

Friday, September 17, 2021 

Fri, Sep 17, 2021 


Friday, September 24, 2021 
Martin Burger  FriedrichAlexander Universität ErlangenNürnberg  A Bregman Learning Framework for Sparse Neural Networks ... more ... less  
Fri, Sep 24, 2021 
Martin Burger, FriedrichAlexander Universität ErlangenNürnberg A Bregman Learning Framework for Sparse Neural Networks ... more ... less 

Abstract: This talk will discuss a novel learning framework based on stochastic Bregman iterations. It allows to train sparse neural networks with an inverse scale space approach, starting from a very sparse network and gradually adding significant parameters. Apart from a baseline algorithm called LinBreg, we will discuss an accelerated version using momentum, and AdaBreg, which is a Bregmanized generalization of the Adam algorithm. Moreover a statistically profound sparse parameter initialization strategy, stochastic convergence analysis of the loss decay, and additional convergence proofs in the convex regime can be derived. The Bregman learning framework can also be applied to Neural Architecture Search, e.g. to unveil autoencoder architectures for denoising or deblurring tasks. 

Abstract: This talk will discuss a novel learning framework based on stochastic Bregman iterations. It allows to train sparse neural networks with an inverse scale space approach, starting from a very sparse network and gradually adding significant parameters. Apart from a baseline algorithm called LinBreg, we will discuss an accelerated version using momentum, and AdaBreg, which is a Bregmanized generalization of the Adam algorithm. Moreover a statistically profound sparse parameter initialization strategy, stochastic convergence analysis of the loss decay, and additional convergence proofs in the convex regime can be derived. The Bregman learning framework can also be applied to Neural Architecture Search, e.g. to unveil autoencoder architectures for denoising or deblurring tasks. 

Friday, October 01, 2021 
Youssef M. Marzouk  MIT  Transport methods for simulationbased inference and data assimilation ... more ... less  
Fri, Oct 01, 2021 
Youssef M. Marzouk, MIT Transport methods for simulationbased inference and data assimilation ... more ... less 

Abstract: Many practical Bayesian inference problems fall into the "likelihoodfree" setting, where evaluations of the likelihood function or prior density are unavailable or intractable; instead one can only simulate (i.e., draw samples from) the associated distributions. I will discuss how transportation of measure can help solve such problems, by constructing maps that push prior samples, or samples from a joint parameterdata prior, to the desired conditional distribution. These methods have broad utility for inference in stochastic and generative models, as well as for data assimilation problems motivated by geophysical applications. Key issues in this construction center on: (1) the estimation of transport maps from few samples; and (2) parameterizations of monotone maps. I will discuss developments on both fronts, including some recent efforts in joint dimension reduction for conditional sampling. As an example, I will present an approach to nonlinear filtering in dynamical systems which uses sparse triangular transport maps to produce robust approximations of the filtering distribution in high dimensions. The approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, and can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. This is joint work with Ricardo Baptista, Alessio Spantini, Olivier Zahm, and Jakob Zech. 

Abstract: Many practical Bayesian inference problems fall into the "likelihoodfree" setting, where evaluations of the likelihood function or prior density are unavailable or intractable; instead one can only simulate (i.e., draw samples from) the associated distributions. I will discuss how transportation of measure can help solve such problems, by constructing maps that push prior samples, or samples from a joint parameterdata prior, to the desired conditional distribution. These methods have broad utility for inference in stochastic and generative models, as well as for data assimilation problems motivated by geophysical applications. Key issues in this construction center on: (1) the estimation of transport maps from few samples; and (2) parameterizations of monotone maps. I will discuss developments on both fronts, including some recent efforts in joint dimension reduction for conditional sampling. As an example, I will present an approach to nonlinear filtering in dynamical systems which uses sparse triangular transport maps to produce robust approximations of the filtering distribution in high dimensions. The approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, and can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. This is joint work with Ricardo Baptista, Alessio Spantini, Olivier Zahm, and Jakob Zech. 

Friday, October 08, 2021 
Sven Leyffer  Argonne National Laboratory  MixedInteger PDEConstrained Optimization ... more ... less  
Fri, Oct 08, 2021 
Sven Leyffer, Argonne National Laboratory MixedInteger PDEConstrained Optimization ... more ... less 

Abstract: Many complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. Examples include the design and control of gas networks, disaster recovery, and topology optimization, and are referred to as mixedinteger PDEconstrained optimization problems, or MIPDECOs. We present the problem of designing an electromagnetic cloak as a MIPDECO with integervalued control inputs that are distributed in the computational domain. We show that the problems can be solved by optimizing only the continuous relaxations of the approximations and then applying a sumup rounding methodology to obtain integervalued controls. These controls are shown to converge and exhibit the desired approximation properties under suitable refinements of the discretizations. We also propose a trustregion method that solves a sequence of linear integer programs to tackle more general integer optimal control problems regularized with a total variation penalty. The total variation penalty allows us to prove the existence of minimizers of the integer optimal control problem, and we present efficient computational techniques for solving these problems. 

Abstract: Many complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. Examples include the design and control of gas networks, disaster recovery, and topology optimization, and are referred to as mixedinteger PDEconstrained optimization problems, or MIPDECOs. We present the problem of designing an electromagnetic cloak as a MIPDECO with integervalued control inputs that are distributed in the computational domain. We show that the problems can be solved by optimizing only the continuous relaxations of the approximations and then applying a sumup rounding methodology to obtain integervalued controls. These controls are shown to converge and exhibit the desired approximation properties under suitable refinements of the discretizations. We also propose a trustregion method that solves a sequence of linear integer programs to tackle more general integer optimal control problems regularized with a total variation penalty. The total variation penalty allows us to prove the existence of minimizers of the integer optimal control problem, and we present efficient computational techniques for solving these problems. 

Friday, October 15, 2021 
Martina Bukac  University of Notre Dame  Adaptive timestepping methods for fluidstructure interaction problems ... more ... less  
Fri, Oct 15, 2021 
Martina Bukac, University of Notre Dame Adaptive timestepping methods for fluidstructure interaction problems ... more ... less 

Abstract: In realistic flow problems described by partial differential equations (PDEs), where the dynamics are not known, or in which the variables are changing rapidly, the robust, adaptive timestepping is central to accurately and efficiently predict the longterm behavior of the solution. This is especially important in the coupled flow problems, such as the fluidstructure interaction (FSI), which often exhibit complex dynamic behavior. While the adaptive spatial mesh refinement techniques are well established and widely used, less attention has been given to the adaptive timestepping methods for PDEs. We will discuss novel, adaptive, partitioned numerical methods for FSI problems with thick and thin structures. The time integration in the proposed methods is based on the refactorized Cauchy's onelegged 'thetalike' method, which consists of a backward Euler method, where the fluid and structure subproblems are subiterated until convergence, followed by a forward Euler method.The bulk of the computation is done by the backward Euler method, as the forward Euler step is equivalent to (and implemented as) a linear extrapolation. We will present the numerical analysis of the proposed methods showing linear convergence of the subiterative process and unconditional stability. The time adaptation strategies will be discussed. The properties of the methods, as well as the selection of the parameters used in the adaptive process, will be explored in numerical examples. 

Abstract: In realistic flow problems described by partial differential equations (PDEs), where the dynamics are not known, or in which the variables are changing rapidly, the robust, adaptive timestepping is central to accurately and efficiently predict the longterm behavior of the solution. This is especially important in the coupled flow problems, such as the fluidstructure interaction (FSI), which often exhibit complex dynamic behavior. While the adaptive spatial mesh refinement techniques are well established and widely used, less attention has been given to the adaptive timestepping methods for PDEs. We will discuss novel, adaptive, partitioned numerical methods for FSI problems with thick and thin structures. The time integration in the proposed methods is based on the refactorized Cauchy's onelegged 'thetalike' method, which consists of a backward Euler method, where the fluid and structure subproblems are subiterated until convergence, followed by a forward Euler method.The bulk of the computation is done by the backward Euler method, as the forward Euler step is equivalent to (and implemented as) a linear extrapolation. We will present the numerical analysis of the proposed methods showing linear convergence of the subiterative process and unconditional stability. The time adaptation strategies will be discussed. The properties of the methods, as well as the selection of the parameters used in the adaptive process, will be explored in numerical examples. 

Friday, October 22, 2021 
Patrick Farrell  University of Oxford  Computing multiple solutions of PDEs with deflation ... more ... less  
Fri, Oct 22, 2021 
Patrick Farrell, University of Oxford Computing multiple solutions of PDEs with deflation ... more ... less 

Abstract: Computing the distinct solutions u of an equation f(u,λ)=0 as a parameter λ ∈ ℝ is varied is a central task in applied mathematics and engineering. The solutions are captured in a bifurcation diagram, plotting (some functional of) u as a function of λ. In this talk I will present a useful idea, deflation, for this task. Deflation has three advantages. First, it is capable of computing disconnected bifurcation diagrams; previous algorithms only aimed to compute that part of the bifurcation diagram continuously connected to the initial data. Second, its implementation is very simple: it only requires a minor modification to an existing Newtonbased solver. Third, it can scale to very large discretisations if a good preconditioner is available; no auxiliary problems must be solved. We will present applications to hyperelastic structures, liquid crystals, and BoseEinstein condensates, and discuss how PDEconstrained optimisation problems may be solved to design systems with certain bifurcation properties. 

Abstract: Computing the distinct solutions u of an equation f(u,λ)=0 as a parameter λ ∈ ℝ is varied is a central task in applied mathematics and engineering. The solutions are captured in a bifurcation diagram, plotting (some functional of) u as a function of λ. In this talk I will present a useful idea, deflation, for this task. Deflation has three advantages. First, it is capable of computing disconnected bifurcation diagrams; previous algorithms only aimed to compute that part of the bifurcation diagram continuously connected to the initial data. Second, its implementation is very simple: it only requires a minor modification to an existing Newtonbased solver. Third, it can scale to very large discretisations if a good preconditioner is available; no auxiliary problems must be solved. We will present applications to hyperelastic structures, liquid crystals, and BoseEinstein condensates, and discuss how PDEconstrained optimisation problems may be solved to design systems with certain bifurcation properties. 

Friday, October 29, 2021 

Fri, Oct 29, 2021 


Friday, November 05, 2021 
no colloquium  
Fri, Nov 05, 2021 
no colloquium  
Friday, November 12, 2021 
Barbara Kaltenbacher  Universität Klagenfurt (AAU)  Reduced, allatonce, and variational formulations of inverse problems and their iterative solution ... more ... less  
Fri, Nov 12, 2021 
Barbara Kaltenbacher, Universität Klagenfurt (AAU) Reduced, allatonce, and variational formulations of inverse problems and their iterative solution ... more ... less 

Abstract: The conventional way of formulating inverse problems such as identification of a (possibly infinite dimensional) parameter, is via some forward operator, which is the concatenation of the observation operator with the parametertostatemap for the underlying model. Recently, allatonce formulations have been considered as an alternative to this reduced formulation, avoiding the use of a parametertostate map, which would sometimes lead to too restrictive conditions. Here the model and the observation are considered simultaneously as one large system with the state and the parameter as unknowns. A still more general formulation of inverse problems, containing both the reduced and the allatonce formulation, but also the wellknown and highly versatile socalled variational approach (not to be mistaken with variational regularization) as special cases, is to formulate the inverse problem as a minimization problem (instead of an equation) for the state and parameter. Regularization can be incorporated via imposing constraints and/or adding regularization terms to the objective. We will consider iterative regularization methods resulting from the application of gradient or Newton type iterations to such minimization based formulations and provide convergence results. In doing so, instead of regularizing the minimization problem and then applying standard iterative optimization methods, we regularize *by* iterating, more precisely by early stopping. 

Abstract: The conventional way of formulating inverse problems such as identification of a (possibly infinite dimensional) parameter, is via some forward operator, which is the concatenation of the observation operator with the parametertostatemap for the underlying model. Recently, allatonce formulations have been considered as an alternative to this reduced formulation, avoiding the use of a parametertostate map, which would sometimes lead to too restrictive conditions. Here the model and the observation are considered simultaneously as one large system with the state and the parameter as unknowns. A still more general formulation of inverse problems, containing both the reduced and the allatonce formulation, but also the wellknown and highly versatile socalled variational approach (not to be mistaken with variational regularization) as special cases, is to formulate the inverse problem as a minimization problem (instead of an equation) for the state and parameter. Regularization can be incorporated via imposing constraints and/or adding regularization terms to the objective. We will consider iterative regularization methods resulting from the application of gradient or Newton type iterations to such minimization based formulations and provide convergence results. In doing so, instead of regularizing the minimization problem and then applying standard iterative optimization methods, we regularize *by* iterating, more precisely by early stopping. 

Friday, November 19, 2021 
Alfred Hero  University of Michigan  Learning to benchmark ... more ... less  
Fri, Nov 19, 2021 
Alfred Hero, University of Michigan Learning to benchmark ... more ... less 

Abstract: We address the problem of learning an achievable lower bound on classification error from a labeled sample. We establish an optimization framework for this metalearning problem, which we call benchmark learning. Benchmark learning leads to an accurate datadriven predictor of an achievable lower bound on misclassification error probability without having to construct any classifier and without assuming any parametric model for the data. The resultant predictor can be used to establish whether it is possible to improve classification performance of any specific classifier. It also yields a stopping rule for sequentially trained classifiers. In addition, The talk will cover relevant background, theory, algorithms, and applications of benchmark learning. 

Abstract: We address the problem of learning an achievable lower bound on classification error from a labeled sample. We establish an optimization framework for this metalearning problem, which we call benchmark learning. Benchmark learning leads to an accurate datadriven predictor of an achievable lower bound on misclassification error probability without having to construct any classifier and without assuming any parametric model for the data. The resultant predictor can be used to establish whether it is possible to improve classification performance of any specific classifier. It also yields a stopping rule for sequentially trained classifiers. In addition, The talk will cover relevant background, theory, algorithms, and applications of benchmark learning. 

Friday, November 26, 2021 
no colloquium (Thanksgiving)  
Fri, Nov 26, 2021 
no colloquium  
Friday, December 03, 2021 
Andrew Gillette  LLNL  Delaunay interpolation diagnostics for model assessment ... more ... less  
Fri, Dec 03, 2021 
Andrew Gillette, LLNL Delaunay interpolation diagnostics for model assessment ... more ... less 

Abstract: Surrogate models, reduced order models, and trained neural networks are now ubiquitous in scientific applications, yet the metrics used to evaluate their accuracy remain heuristic and applicationdependent. We will address the challenge of model assessment from the perspective of function approximation: given only the ability to evaluate a function f : R^d > R^k on some set of inputs from R^d, what can we conclude about the properties of f itself? Using a scalable Delaunaybased interpolation method, we build a sequence of piecewise linear approximations of f and compute their rate of convergence. The technique is inspired by classical a priori convergence error estimates for finite element methods. Initial results indicate this rate can help identify multiscale behavior, requisite sampling density, and regions of nearlinearity in a model. We will discuss applications of the approach to the iterative selection of parameters for inertial confinement fusion simulations carried out at LLNL. About the speaker:Andrew Gillette is a computational mathematician at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. He works on a variety of projects involving numerical methods for PDEs, highdimensional function approximation, computational geometry, and machine learning. Prior to joining CASC in 2019, Andrew was an Associate Professor of Mathematics at the University of Arizona. 

Abstract: Surrogate models, reduced order models, and trained neural networks are now ubiquitous in scientific applications, yet the metrics used to evaluate their accuracy remain heuristic and applicationdependent. We will address the challenge of model assessment from the perspective of function approximation: given only the ability to evaluate a function f : R^d > R^k on some set of inputs from R^d, what can we conclude about the properties of f itself? Using a scalable Delaunaybased interpolation method, we build a sequence of piecewise linear approximations of f and compute their rate of convergence. The technique is inspired by classical a priori convergence error estimates for finite element methods. Initial results indicate this rate can help identify multiscale behavior, requisite sampling density, and regions of nearlinearity in a model. We will discuss applications of the approach to the iterative selection of parameters for inertial confinement fusion simulations carried out at LLNL. About the speaker:Andrew Gillette is a computational mathematician at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. He works on a variety of projects involving numerical methods for PDEs, highdimensional function approximation, computational geometry, and machine learning. Prior to joining CASC in 2019, Andrew was an Associate Professor of Mathematics at the University of Arizona. 
Spring 2021 ... hide
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title  
Friday, January 29, 2021 
Irene Fonseca  Carnegie Mellon University  Geometric Flows and Phase Transitions in Heterogeneous Media ... more ... less  
Fri, Jan 29, 2021 
Irene Fonseca, Carnegie Mellon University Geometric Flows and Phase Transitions in Heterogeneous Media ... more ... less 

Abstract: We present the first, unconditional convergence results for an AllenCahn type bistable reaction diffusion equation in a periodic medium. Our limiting dynamics are given by an analog for anisotropic mean curvature flow, of the formulation due to Ken Brakke. As an essential ingredient in the analysis, we obtain an explicit expression for the effective surface tension, which dictates the limiting anisotropic mean curvature. This is joint work with Rustum Choksi (McGill), Jessica Lin (McGill), and Raghavendra Venkatraman (CMU). 

Abstract: We present the first, unconditional convergence results for an AllenCahn type bistable reaction diffusion equation in a periodic medium. Our limiting dynamics are given by an analog for anisotropic mean curvature flow, of the formulation due to Ken Brakke. As an essential ingredient in the analysis, we obtain an explicit expression for the effective surface tension, which dictates the limiting anisotropic mean curvature. This is joint work with Rustum Choksi (McGill), Jessica Lin (McGill), and Raghavendra Venkatraman (CMU). 

Friday, February 05, 2021 
Georg Stadler  New York University  Estimation of extreme event probabilities in systems governed by PDEs ... more ... less  
Fri, Feb 05, 2021 
Georg Stadler, New York University Estimation of extreme event probabilities in systems governed by PDEs ... more ... less 

Abstract: We propose methods for the estimation of extreme event probabilities in complex systems governed by PDEs. Our approach is guided by ideas from large deviation theory (LDT) and borrows methods from PDEconstrained optimization. The systems under consideration involve random parameters and we are interested in quantifying the probability that a scalar function of the system state is at or above a threshold. The proposed methods initially solve an optimization problem over the set of parameters leading to events above a threshold. Based on solutions of this PDEconstrained optimization problem, we propose (1) an importance sampling method and (2) a method that uses curvature information of the extreme event boundary to estimate small probabilities. We illustrate the application of our approach to quantify the probability of extreme tsunami events on shore. Tsunamis are typically caused by a sudden, unpredictable change of the ocean floor elevation during an earthquake. We model this change as random process and use the onedimensional shallow water equation to model tsunamis. The PDEconstrained optimization problem arising in this application is governed by the shallow water equation. This is joint work with Shanyin Tong and Eric VandenEijnden from NYU. 

Abstract: We propose methods for the estimation of extreme event probabilities in complex systems governed by PDEs. Our approach is guided by ideas from large deviation theory (LDT) and borrows methods from PDEconstrained optimization. The systems under consideration involve random parameters and we are interested in quantifying the probability that a scalar function of the system state is at or above a threshold. The proposed methods initially solve an optimization problem over the set of parameters leading to events above a threshold. Based on solutions of this PDEconstrained optimization problem, we propose (1) an importance sampling method and (2) a method that uses curvature information of the extreme event boundary to estimate small probabilities. We illustrate the application of our approach to quantify the probability of extreme tsunami events on shore. Tsunamis are typically caused by a sudden, unpredictable change of the ocean floor elevation during an earthquake. We model this change as random process and use the onedimensional shallow water equation to model tsunamis. The PDEconstrained optimization problem arising in this application is governed by the shallow water equation. This is joint work with Shanyin Tong and Eric VandenEijnden from NYU. 

Friday, February 12, 2021 
Eric VandenEijnden  New York University  Trainability and accuracy of artificial neural networks ... more ... less  
Fri, Feb 12, 2021 
Eric VandenEijnden, New York University Trainability and accuracy of artificial neural networks ... more ... less 

Abstract: The recent success of machine learning suggests that neural networks may be capable of approximating highdimensional functions with controllably small errors. As a result, they could outperform standard function interpolation methods that have been the workhorses of scientific computing but do not scale well with dimension. In support of this prospect, here I will review what is known about the trainability and accuracy of shallow neural networks, which offer the simplest instance of nonlinear learning in functional spaces that are fundamentally different from classic approximation spaces. The dynamics of training in these spaces can be analyzed using tools from optimal transport and statistical mechanics, which reveal when and how shallow neural networks can overcome the curse of dimensionality. I will also discuss how scientific computing problem in highdimension once thought intractable can be revisited through the lens of these results. Finally, I will discuss open questions, including potential generalizations to deep architecture. This talk is based on joint work with Grant Rotskoff, Joan Bruna, Zhengdao Chen, and Sammy Jelassi. 

Abstract: The recent success of machine learning suggests that neural networks may be capable of approximating highdimensional functions with controllably small errors. As a result, they could outperform standard function interpolation methods that have been the workhorses of scientific computing but do not scale well with dimension. In support of this prospect, here I will review what is known about the trainability and accuracy of shallow neural networks, which offer the simplest instance of nonlinear learning in functional spaces that are fundamentally different from classic approximation spaces. The dynamics of training in these spaces can be analyzed using tools from optimal transport and statistical mechanics, which reveal when and how shallow neural networks can overcome the curse of dimensionality. I will also discuss how scientific computing problem in highdimension once thought intractable can be revisited through the lens of these results. Finally, I will discuss open questions, including potential generalizations to deep architecture. This talk is based on joint work with Grant Rotskoff, Joan Bruna, Zhengdao Chen, and Sammy Jelassi. 

Friday, February 19, 2021 
Eric Darve  Stanford University  2nd order optimizers for physicsinformed learning. Time: 11:15 am EST ... more ... less 

Fri, Feb 19, 2021 
Eric Darve, Stanford University 2nd order optimizers for physicsinformed learning. 

Abstract: physicsinformed learning is a new class of deep learning algorithms that combine deep neural networks and numerical partial differential equation (PDE) solvers based on physical models. Although very promising, these algorithms require the accurate solution of often illconditioned optimization problems in highdimension. 1st order optimizers like the stochastic gradient descent and ADAM have proven very successful for many machine learning applications but typically exhibit weaker performance on physicsinformed learning tasks. Instead, 2nd order methods like BFGS and trustregion methods are much more robust and efficient for these problems. In this talk, we will discuss the performance and requirements of these optimizers for physicsinformed learning tasks for different types of PDEs. 

Abstract: physicsinformed learning is a new class of deep learning algorithms that combine deep neural networks and numerical partial differential equation (PDE) solvers based on physical models. Although very promising, these algorithms require the accurate solution of often illconditioned optimization problems in highdimension. 1st order optimizers like the stochastic gradient descent and ADAM have proven very successful for many machine learning applications but typically exhibit weaker performance on physicsinformed learning tasks. Instead, 2nd order methods like BFGS and trustregion methods are much more robust and efficient for these problems. In this talk, we will discuss the performance and requirements of these optimizers for physicsinformed learning tasks for different types of PDEs. 

Friday, February 26, 2021 
Alexis F. Vasseur  University of Texas at Austin  Stability of discontinuous solutions for inviscid compressible flows ... more ... less  
Fri, Feb 26, 2021 
Alexis F. Vasseur, University of Texas at Austin Stability of discontinuous solutions for inviscid compressible flows ... more ... less 

Abstract: We will discuss recent developments of the theory of acontraction with shifts to study the stability of discontinuous solutions of systems of equations modeling inviscid compressible flows, like the compressible Euler equation. In the general setting, the only stability result for multiD shocks (Majda, 1981) involves very regular perturbations. More recently, the convex integration method showed that they are not stable under wild $L^2$ perturbations. In the onedimensional configuration, a consequence of the Bressan theory shows that shocks are stable under small BV perturbations (together with a technical condition known as bounded variations on spacelike curve). The theory of acontraction allows to extend the Bressan theory to a weak/BV stability result allowing wild perturbations fulfilling only the socalled strong trace property. Another way to study the stability of inviscid shock is through inviscid limit of viscous models. In one dimension, the study of the so called ”artificial” viscosity limit, is now well understood. However, progress on the vanishing ”physical” viscosity limit (for instance, from compressible NavierStokes systems to inviscid limit of Compressible Euler equations) has been far slower. One of the big recent success of the theory of acontraction with shifts, is the stability of viscous shocks subject to large perturbations. Stability results on the inviscid model are then inherited at the inviscid limit, thanks to the fact that large perturbations, independent of the viscosity, can be considered at the NavierStokes level. These stability results hold in the class of wild perturbations of inviscid limits, without any regularity restriction (not even strong trace property). This shows that the class of inviscid limits of NavierStokes equations is better behaved that the class of weak solutions to the inviscid limit problem. A first multiD result of stability of contact discontinuities without shear, in the class of inviscid limit of FourierNavierStokes, shows that the same property is true for some situations even in multiD. 

Abstract: We will discuss recent developments of the theory of acontraction with shifts to study the stability of discontinuous solutions of systems of equations modeling inviscid compressible flows, like the compressible Euler equation. In the general setting, the only stability result for multiD shocks (Majda, 1981) involves very regular perturbations. More recently, the convex integration method showed that they are not stable under wild $L^2$ perturbations. In the onedimensional configuration, a consequence of the Bressan theory shows that shocks are stable under small BV perturbations (together with a technical condition known as bounded variations on spacelike curve). The theory of acontraction allows to extend the Bressan theory to a weak/BV stability result allowing wild perturbations fulfilling only the socalled strong trace property. Another way to study the stability of inviscid shock is through inviscid limit of viscous models. In one dimension, the study of the so called ”artificial” viscosity limit, is now well understood. However, progress on the vanishing ”physical” viscosity limit (for instance, from compressible NavierStokes systems to inviscid limit of Compressible Euler equations) has been far slower. One of the big recent success of the theory of acontraction with shifts, is the stability of viscous shocks subject to large perturbations. Stability results on the inviscid model are then inherited at the inviscid limit, thanks to the fact that large perturbations, independent of the viscosity, can be considered at the NavierStokes level. These stability results hold in the class of wild perturbations of inviscid limits, without any regularity restriction (not even strong trace property). This shows that the class of inviscid limits of NavierStokes equations is better behaved that the class of weak solutions to the inviscid limit problem. A first multiD result of stability of contact discontinuities without shear, in the class of inviscid limit of FourierNavierStokes, shows that the same property is true for some situations even in multiD. 

Friday, March 05, 2021 
no colloquium  
Fri, Mar 05, 2021 
no colloquium  
Friday, March 12, 2021 
XueCheng Tai  Hong Kong Baptist University  The Softmax function, Potts model and variational neural networks ... more ... less  
Fri, Mar 12, 2021 
XueCheng Tai, Hong Kong Baptist University The Softmax function, Potts model and variational neural networks ... more ... less 

Abstract: In this talk, we present our recent research on using variational models as layers for deep neural networks (DNNs). We use image segmentation as an example. The technique can also be used for high dimensional data classification as well. Through this technique, we could integrate many wellknow variational models for image segmentation into deep neural networks. The new networks will have the advantages of traditional DNNs. At the same time, the outputs from the new networks can also have many good properties of variational models for image segmentation. We will present some techniques to incorporate shape priors into the networks through the variational layers. We will show how to design networks with spatial regularization and volume preservation. We can also design networks with guarantee that the output shapes from the network for image segmentation must be convex shapes/starshapes. It is numerically verified that these techniques can improve the performance when the true shapes satisfy these priors. The ideas of these new networks is based on some relationship between the softmax function, the Potts models and the structure of traditional DNNs. We will explain this in detail which leads naturally to the newly designed networks. This talk is based on joint works with Jun Liu, S. Luo and several other collaborators. 

Abstract: In this talk, we present our recent research on using variational models as layers for deep neural networks (DNNs). We use image segmentation as an example. The technique can also be used for high dimensional data classification as well. Through this technique, we could integrate many wellknow variational models for image segmentation into deep neural networks. The new networks will have the advantages of traditional DNNs. At the same time, the outputs from the new networks can also have many good properties of variational models for image segmentation. We will present some techniques to incorporate shape priors into the networks through the variational layers. We will show how to design networks with spatial regularization and volume preservation. We can also design networks with guarantee that the output shapes from the network for image segmentation must be convex shapes/starshapes. It is numerically verified that these techniques can improve the performance when the true shapes satisfy these priors. The ideas of these new networks is based on some relationship between the softmax function, the Potts models and the structure of traditional DNNs. We will explain this in detail which leads naturally to the newly designed networks. This talk is based on joint works with Jun Liu, S. Luo and several other collaborators. 

Friday, March 19, 2021 
Michael Hintermüller  WIAS and HumboldtUniversität zu Berlin  Optimization with learninginformed differential equation constraints and its applications ... more ... less  
Fri, Mar 19, 2021 
Michael Hintermüller, WIAS and HumboldtUniversität zu Berlin Optimization with learninginformed differential equation constraints and its applications ... more ... less 

Abstract: Inspired by applications in optimal control of semilinear elliptic partial differential equations and physicsintegrated imaging, differential equation constrained optimization problems with constituents that are only accessible through datadriven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machinelearned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided. Joint work with G. Dong and K. Papafitsoros (both Weierstrass Institute Berlin) 

Abstract: Inspired by applications in optimal control of semilinear elliptic partial differential equations and physicsintegrated imaging, differential equation constrained optimization problems with constituents that are only accessible through datadriven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machinelearned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided. Joint work with G. Dong and K. Papafitsoros (both Weierstrass Institute Berlin) 

Friday, March 26, 2021 
Anders Petersson  Lawerence Livermore National Lab  Numerical Optimal Control of Quantum Systems. Time: 11:30 am EST ... more ... less 

Fri, Mar 26, 2021 
Anders Petersson, Lawerence Livermore National Lab Numerical Optimal Control of Quantum Systems. 

Abstract: 

Abstract: 

Friday, April 02, 2021 
East Cost Optimization Meeting (ECOM)  George Mason University  
Fri, Apr 02, 2021 
East Cost Optimization Meeting (ECOM), George Mason University


Friday, April 09, 2021 
Roland Herzog  Chemnitz University of Technology  Total Variation and Total Generalized Variation: From Optimal Control to Geometry Processing ... more ... less  
Fri, Apr 09, 2021 
Roland Herzog, Chemnitz University of Technology Total Variation and Total Generalized Variation: From Optimal Control to Geometry Processing ... more ... less 

Abstract: The total variation (TV) seminorm is popular as a regularizing functional in inverse problems and imaging, favoring piecewise constant functions. As an extension, Bredies, Kunisch and Pock introduced the total generalized variation (TGV), which favors piecewise linear (or higherorder) polynomials. In this presentation, we address discrete TV and TGV models for finite element formulations and their use in optimal control, imaging, and geometry processing applications, along with tailored optimization algorithms. 

Abstract: The total variation (TV) seminorm is popular as a regularizing functional in inverse problems and imaging, favoring piecewise constant functions. As an extension, Bredies, Kunisch and Pock introduced the total generalized variation (TGV), which favors piecewise linear (or higherorder) polynomials. In this presentation, we address discrete TV and TGV models for finite element formulations and their use in optimal control, imaging, and geometry processing applications, along with tailored optimization algorithms. 

Friday, April 16, 2021 
Youngsoo Choi  LLNL  Where are we with datadriven surrogate modeling for various physical simulations? ... more ... less  
Fri, Apr 16, 2021 
Youngsoo Choi, LLNL Where are we with datadriven surrogate modeling for various physical simulations? ... more ... less 

Abstract: A surrogate model is built to accelerate computationally expensive physical simulations, which is useful in multiquery problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of datadriven surrogate modeling techniques will be discussed, i.e., the blackbox approach that incorporates only data and the physicsinformed approach that incorporates the physics information as well as data within the surrogate models. The advantages and disadvantages of each method will be discussed. Furthermore, several recent developments at LLNL of datadriven physicsinformed surrogate modeling techniques will be introduced in the context of various physical simulations. For example, the timewindowing reduced order model overcomes the difficulty of shock propagation phenomenon, achieving a speedup of O(2~10) with a relative error less than 1% for relatively small Lagrangian hydrodynamics problems. The space–time reduced order model accelerates largescale Neutron transport simulations by a factor of 7,000 with a relative error less than 1%. The nonlinear manifold reduced order model shows perfect marriage between machine learning and physicsinformed surrogate modeling and also solves the challenge imposed by the advectiondominated physical simulations. Finally, successful application of these surrogate models in design optimization settings will be presented. About the speaker:Youngsoo is a computational scientist in CASC under Computing directorate. His research focus lies on developing efficient reduced order models for various physical simulations to be used in multiquery problems, such as inverse problems, design optimization, and uncertainty quantification. He is currently leading datadriven surrogate model development team for various physical simulations. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017. 

Abstract: A surrogate model is built to accelerate computationally expensive physical simulations, which is useful in multiquery problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of datadriven surrogate modeling techniques will be discussed, i.e., the blackbox approach that incorporates only data and the physicsinformed approach that incorporates the physics information as well as data within the surrogate models. The advantages and disadvantages of each method will be discussed. Furthermore, several recent developments at LLNL of datadriven physicsinformed surrogate modeling techniques will be introduced in the context of various physical simulations. For example, the timewindowing reduced order model overcomes the difficulty of shock propagation phenomenon, achieving a speedup of O(2~10) with a relative error less than 1% for relatively small Lagrangian hydrodynamics problems. The space–time reduced order model accelerates largescale Neutron transport simulations by a factor of 7,000 with a relative error less than 1%. The nonlinear manifold reduced order model shows perfect marriage between machine learning and physicsinformed surrogate modeling and also solves the challenge imposed by the advectiondominated physical simulations. Finally, successful application of these surrogate models in design optimization settings will be presented. About the speaker:Youngsoo is a computational scientist in CASC under Computing directorate. His research focus lies on developing efficient reduced order models for various physical simulations to be used in multiquery problems, such as inverse problems, design optimization, and uncertainty quantification. He is currently leading datadriven surrogate model development team for various physical simulations. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017. 

Friday, April 23, 2021 
Jan S Hesthaven  EPFL  Nonintrusive reduced order models using physics informed neural networks ... more ... less  
Fri, Apr 23, 2021 
Jan S Hesthaven, EPFL Nonintrusive reduced order models using physics informed neural networks ... more ... less 

Abstract: The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sampled over a potentially large parameter space, such as in optimization, control, uncertainty quantification, and in applications where a near realtime response is needed. However, many challenges remain unresolved to secure the flexibility, robustness, and efficiency needed for general largescale applications, in particular for nonlinear and/or timedependent problems. After giving a brief general introduction to projection based reduced order models, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications. In the second part of the talk, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model. Time permitting, we finally discuss the use of reduced order models in the context of prediction, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the MoriZwansig formulation for timedependent problems, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model. 

Abstract: The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sampled over a potentially large parameter space, such as in optimization, control, uncertainty quantification, and in applications where a near realtime response is needed. However, many challenges remain unresolved to secure the flexibility, robustness, and efficiency needed for general largescale applications, in particular for nonlinear and/or timedependent problems. After giving a brief general introduction to projection based reduced order models, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications. In the second part of the talk, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model. Time permitting, we finally discuss the use of reduced order models in the context of prediction, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the MoriZwansig formulation for timedependent problems, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model. 

Friday, April 30, 2021 
Karl Kunisch  University of Graz  Semiglobal optimal Feedback stabilization of autonomous systems via deep neural network approximation ... more ... less  
Fri, Apr 30, 2021 
Karl Kunisch, University of Graz Semiglobal optimal Feedback stabilization of autonomous systems via deep neural network approximation ... more ... less 

Abstract: A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach rests on two main ingredients. First, an optimal control formulation involving an ensemble of trajectories with 'control' variables given by the feedback gain functions. Second, an approximation to the feedback functions via realizations by neural networks. Based on universal approximation properties we prove the existence and convergence of optimal stabilizing neural network feedback controllers. The talk is based on joint work with Daniel Walter. 

Abstract: A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach rests on two main ingredients. First, an optimal control formulation involving an ensemble of trajectories with 'control' variables given by the feedback gain functions. Second, an approximation to the feedback functions via realizations by neural networks. Based on universal approximation properties we prove the existence and convergence of optimal stabilizing neural network feedback controllers. The talk is based on joint work with Daniel Walter. 

Friday, May 07, 2021 
Robert F. Dejaco  NIST  Resolving the Shock Layer in FixedBed Adsorption with Boundary Layer Theory ... more ... less  
Fri, May 07, 2021 
Robert F. Dejaco, NIST Resolving the Shock Layer in FixedBed Adsorption with Boundary Layer Theory ... more ... less 

Abstract: In adsorption separations, mixtures flow through a column packed with solid particles. The weakly adsorbing component moves faster than the strongly adsorbing component, causing the exiting mixture to separate relative to the inlet. By exploiting differences in affinity for a solid material, rather than heating and cooling (e.g., conventional distillation), adsorption separations can be very energy efficient. Understanding the socalled “breakthrough curve” measurement – the outlet fluid concentrations as a function of time – is central to efficient industrial implementation. Mathematical modeling of the associated nonlinear PDE can provide a quantitative connection between the characteristics of the adsorbent material and the breakthrough curve measurement. We apply boundary layer theory to study breakthrough curve measurements for isothermal singlesolute adsorption with plug flow in the limit of fast adsorption compared to convection. Our perturbation theory connects two seemingly unrelated theories, one assuming infinitely fast mass transfer and the other an infinitely long column. The leading order “outer” form of the problem is a conservation law that yields shock waves via the method of characteristics. The discontinuity at the shock can be resolved by rescaling in a moving coordinate system. Analysis of the boundary layer reveals that the associated breakthrough curve has exactly one inflection point, is not necessarily symmetric, and only occurs when the relationship for solute partitioning adopts a certain convexity. A comparison to numerical simulations is presented to support the validity of the approach. 

Abstract: In adsorption separations, mixtures flow through a column packed with solid particles. The weakly adsorbing component moves faster than the strongly adsorbing component, causing the exiting mixture to separate relative to the inlet. By exploiting differences in affinity for a solid material, rather than heating and cooling (e.g., conventional distillation), adsorption separations can be very energy efficient. Understanding the socalled “breakthrough curve” measurement – the outlet fluid concentrations as a function of time – is central to efficient industrial implementation. Mathematical modeling of the associated nonlinear PDE can provide a quantitative connection between the characteristics of the adsorbent material and the breakthrough curve measurement. We apply boundary layer theory to study breakthrough curve measurements for isothermal singlesolute adsorption with plug flow in the limit of fast adsorption compared to convection. Our perturbation theory connects two seemingly unrelated theories, one assuming infinitely fast mass transfer and the other an infinitely long column. The leading order “outer” form of the problem is a conservation law that yields shock waves via the method of characteristics. The discontinuity at the shock can be resolved by rescaling in a moving coordinate system. Analysis of the boundary layer reveals that the associated breakthrough curve has exactly one inflection point, is not necessarily symmetric, and only occurs when the relationship for solute partitioning adopts a certain convexity. A comparison to numerical simulations is presented to support the validity of the approach. 

Danielle C. Brager  NIST  Mathematically investigating Retinitis Pigmentosa ... more ... less  
Danielle C. Brager, NIST Mathematically investigating Retinitis Pigmentosa ... more ... less 

Abstract: Retinitis Pigmentosa (RP) is a collection of clinically and genetically heterogeneous degenerative retinal diseases. Patients with RP experience a loss of night vision that progresses to daylight blindness due to the sequential degeneration of rod and cone photoreceptors. While known genetic mutations associated with RP affect the rods, the degeneration of cones inevitably follows in a manner independent of those genetic mutations. Investigation of this secondary death of cone photoreceptors led to the discovery of the rodderived cone viability factor (RdCVF), a protein secreted by the rods that preserves the cones by accelerating the flow of glucose into cone cells stimulating aerobic glycolysis. In this work, we formulate a predatorprey style system of nonlinear ordinary differential equations to mathematically model photoreceptor interactions in the presence of RP while accounting for the new understanding of RdCVF's role in enhancing cone survival. We utilize the mathematical model and subsequent analysis to examine the underlying processes and mechanisms (defined by the model parameters) that affect cone photoreceptor vitality as RP progresses. The physiologically relevant equilibrium points are interpreted as different stages of retinal degeneration. We determine conditions necessary for the local asymptotic stability of these equilibrium points and use the results as criteria needed to remain in a stage in the progression of retinal degeneration. Experimental data is used for parameter estimation. Pathways to blindness are uncovered via bifurcations and narrows our focus to four of the model equilibria. Using Latin Hypercube Sampling coupled with partial rank correlation coefficients, we perform a sensitivity analysis to determine mechanisms that have a significant effect on the cones at four stages of RP. We derive a nondimensional form of the mathematical model and perform a numerical bifurcation analysis using MATCONT to explore the existence of stable limit cycles because a stable limit cycle is a stable mode, other than an equilibrium point, where the rods and cones coexist. In our analyses, a set of key parameters involved in photoreceptor outer segment shedding, renewal, and nutrient supply were shown to govern the dynamics of the system. Our findings illustrate the benefit of using mathematical models to uncover mechanisms driving the progression of RP and opens the possibility to use in silico experiments to test treatment options in the absence of rods. 

Abstract: Retinitis Pigmentosa (RP) is a collection of clinically and genetically heterogeneous degenerative retinal diseases. Patients with RP experience a loss of night vision that progresses to daylight blindness due to the sequential degeneration of rod and cone photoreceptors. While known genetic mutations associated with RP affect the rods, the degeneration of cones inevitably follows in a manner independent of those genetic mutations. Investigation of this secondary death of cone photoreceptors led to the discovery of the rodderived cone viability factor (RdCVF), a protein secreted by the rods that preserves the cones by accelerating the flow of glucose into cone cells stimulating aerobic glycolysis. In this work, we formulate a predatorprey style system of nonlinear ordinary differential equations to mathematically model photoreceptor interactions in the presence of RP while accounting for the new understanding of RdCVF's role in enhancing cone survival. We utilize the mathematical model and subsequent analysis to examine the underlying processes and mechanisms (defined by the model parameters) that affect cone photoreceptor vitality as RP progresses. The physiologically relevant equilibrium points are interpreted as different stages of retinal degeneration. We determine conditions necessary for the local asymptotic stability of these equilibrium points and use the results as criteria needed to remain in a stage in the progression of retinal degeneration. Experimental data is used for parameter estimation. Pathways to blindness are uncovered via bifurcations and narrows our focus to four of the model equilibria. Using Latin Hypercube Sampling coupled with partial rank correlation coefficients, we perform a sensitivity analysis to determine mechanisms that have a significant effect on the cones at four stages of RP. We derive a nondimensional form of the mathematical model and perform a numerical bifurcation analysis using MATCONT to explore the existence of stable limit cycles because a stable limit cycle is a stable mode, other than an equilibrium point, where the rods and cones coexist. In our analyses, a set of key parameters involved in photoreceptor outer segment shedding, renewal, and nutrient supply were shown to govern the dynamics of the system. Our findings illustrate the benefit of using mathematical models to uncover mechanisms driving the progression of RP and opens the possibility to use in silico experiments to test treatment options in the absence of rods. 
Fall 2020 ... hide
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title  
Friday, August 28, 2020 
Enrique Zuazua  University of Erlangen–Nuremberg (FAU)  Turnpike control and deep learning ... more ... less  
Fri, Aug 28, 2020 
Enrique Zuazua, University of Erlangen–Nuremberg (FAU) Turnpike control and deep learning ... more ... less 

Abstract: The turnpike principle asserts that in long time horizons optimal control strategies are nearly of a steady state nature. In this lecture we shall survey on some recent results on this topic and present some its consequences on deep supervised learning. This lecture will be based in particular on recent joint work with C: Esteve, B. Geshkovski and D. Pighin. arxiv 

Abstract: The turnpike principle asserts that in long time horizons optimal control strategies are nearly of a steady state nature. In this lecture we shall survey on some recent results on this topic and present some its consequences on deep supervised learning. This lecture will be based in particular on recent joint work with C: Esteve, B. Geshkovski and D. Pighin. arxiv 

Friday, September 04, 2020 
Rainald Löhner  George Mason University  Modeling and Simulation of Viral Propagation in the Built Environment ... more ... less  
Fri, Sep 04, 2020 
Rainald Löhner, George Mason University Modeling and Simulation of Viral Propagation in the Built Environment ... more ... less 

Abstract: This talk will begin by summarizing mechanical characteristics of virus contaminants and the transmission via droplets and aerosols. The ordinary and partial differential equations describing the physics of these processes with high fidelity will be presented. We shall also describe the appropriate numerical schemes to solve these problems. We will conclude the talk with several realistic examples of the built environments, such as TSA Queues, Hospital Rooms. DOI 

Abstract: This talk will begin by summarizing mechanical characteristics of virus contaminants and the transmission via droplets and aerosols. The ordinary and partial differential equations describing the physics of these processes with high fidelity will be presented. We shall also describe the appropriate numerical schemes to solve these problems. We will conclude the talk with several realistic examples of the built environments, such as TSA Queues, Hospital Rooms. DOI 

Friday, September 11, 2020 
Fioralba Cakoni  Rutgers University  Spectral Problems in Inverse Scattering for Inhomogeneous Media ... more ... less  
Fri, Sep 11, 2020 
Fioralba Cakoni, Rutgers University Spectral Problems in Inverse Scattering for Inhomogeneous Media ... more ... less 

Abstract: The inverse scattering problem for inhomogeneous media amounts to inverting a locally compact nonlinear operator, thus presenting difficulties in arriving at a solution. Initial efforts to deal with the nonlinear and illposed nature of the inverse scattering problem focused on the use of nonlinear optimization methods. Although efficient in many situations, their use suffers from the need for strong a priori information in order to implement such an approach. In addition, recent advances in material science and nanostructure fabrications have introduced new exotic materials for which full reconstruction of the constitutive parameters from scattering data is challenging or even impossible. In order to circumvent these difficulties, a recent trend in inverse scattering theory has focused on the development of new methods, in which the amount of a priori information needed is drastically reduced but at the expense of obtaining only limited information of the scatterers. Such methods come under the general title of qualitative approach in inverse scattering theory; they yield mathematically justified and computationally simple reconstruction algorithms by investigating properties of the linear scattering operator to decode nonlinear information about the scattering object. In this spirit, a possible approach is to exploit spectral properties of operators associated with scattering phenomena which carry essential information about the media. The identified eigenvalues must satisfy two important properties: 1) can be determined from the scattering operator, and 2) are related to geometrical and physical properties of the media in an understandable way. In this talk we will discuss some old and new eigenvalue problems arising in scattering theory for inhomogeneous media. We will present a twofold discussion: on one hand relating the eigenvalues to the measurement operator (to address the first property) and on the other hand viewing them as the spectrum of appropriate (possibly nonselfadjoint) partial differential operators (to address the second property). Numerical examples will be presented to show what kind of information these eigenvalues, and more generally the qualitative approach, yield on the unknown inhomogeneity. 

Abstract: The inverse scattering problem for inhomogeneous media amounts to inverting a locally compact nonlinear operator, thus presenting difficulties in arriving at a solution. Initial efforts to deal with the nonlinear and illposed nature of the inverse scattering problem focused on the use of nonlinear optimization methods. Although efficient in many situations, their use suffers from the need for strong a priori information in order to implement such an approach. In addition, recent advances in material science and nanostructure fabrications have introduced new exotic materials for which full reconstruction of the constitutive parameters from scattering data is challenging or even impossible. In order to circumvent these difficulties, a recent trend in inverse scattering theory has focused on the development of new methods, in which the amount of a priori information needed is drastically reduced but at the expense of obtaining only limited information of the scatterers. Such methods come under the general title of qualitative approach in inverse scattering theory; they yield mathematically justified and computationally simple reconstruction algorithms by investigating properties of the linear scattering operator to decode nonlinear information about the scattering object. In this spirit, a possible approach is to exploit spectral properties of operators associated with scattering phenomena which carry essential information about the media. The identified eigenvalues must satisfy two important properties: 1) can be determined from the scattering operator, and 2) are related to geometrical and physical properties of the media in an understandable way. In this talk we will discuss some old and new eigenvalue problems arising in scattering theory for inhomogeneous media. We will present a twofold discussion: on one hand relating the eigenvalues to the measurement operator (to address the first property) and on the other hand viewing them as the spectrum of appropriate (possibly nonselfadjoint) partial differential operators (to address the second property). Numerical examples will be presented to show what kind of information these eigenvalues, and more generally the qualitative approach, yield on the unknown inhomogeneity. 

Friday, September 18, 2020 
Shawn Walker  Louisiana State University  Mathematical Modeling and Numerics for Nematic Liquid Crystals ... more ... less  
Fri, Sep 18, 2020 
Shawn Walker, Louisiana State University Mathematical Modeling and Numerics for Nematic Liquid Crystals ... more ... less 

Abstract: I start with an overview of nematic liquid crystals (LCs), including their basic physics, applications, and how they are modeled. In particular, I describe different models, such as OseenFrank, Landaude Gennes, and the Ericksen model, as well as their numerical discretization. In addition, I give the advantages and disadvantages of each model. For the rest of the talk, I will focus on Landaude Gennes (LdG) and Ericksen. Next, I will highlight parts of the analysis of these models and how it relates to numerical analysis, with specific emphasis on finite element methods (FEMs) to compute energy minimizers; much of this work is joint with various coauthors which I will review. I will illustrate the methods we have developed by presenting numerical simulations in two and three dimensions including nonorientable line fields (LdG model). Finally, I will conclude with some current problems in modeling and simulating LCs and an outlook to future directions. 

Abstract: I start with an overview of nematic liquid crystals (LCs), including their basic physics, applications, and how they are modeled. In particular, I describe different models, such as OseenFrank, Landaude Gennes, and the Ericksen model, as well as their numerical discretization. In addition, I give the advantages and disadvantages of each model. For the rest of the talk, I will focus on Landaude Gennes (LdG) and Ericksen. Next, I will highlight parts of the analysis of these models and how it relates to numerical analysis, with specific emphasis on finite element methods (FEMs) to compute energy minimizers; much of this work is joint with various coauthors which I will review. I will illustrate the methods we have developed by presenting numerical simulations in two and three dimensions including nonorientable line fields (LdG model). Finally, I will conclude with some current problems in modeling and simulating LCs and an outlook to future directions. 

Friday, September 25, 2020 
CarolaBibiane Schönlieb  University of Cambridge  Multitasking inverse problems: more together than alone ... more ... less  
Fri, Sep 25, 2020 
CarolaBibiane Schönlieb, University of Cambridge Multitasking inverse problems: more together than alone ... more ... less 

Abstract: Inverse imaging problems in practice constitute a pipeline of tasks that starts with image reconstruction, involves registration, segmentation, and a prediction task at the end. The idea of multitasking inverse problems is to make use of the full information in the data in every step of this pipeline by jointly optimising for all tasks. While this is not a new idea in inverse problems, the ability of deep learning to capture complex prior information paired with its computational efficiency renders an allinone approach practically possible for the first time. 

Abstract: Inverse imaging problems in practice constitute a pipeline of tasks that starts with image reconstruction, involves registration, segmentation, and a prediction task at the end. The idea of multitasking inverse problems is to make use of the full information in the data in every step of this pipeline by jointly optimising for all tasks. While this is not a new idea in inverse problems, the ability of deep learning to capture complex prior information paired with its computational efficiency renders an allinone approach practically possible for the first time. 

Friday, October 02, 2020 
Drew P. Kouri  Sandia National Laboratories  Randomized Sketching for LowMemory Dynamic Optimization ... more ... less  
Fri, Oct 02, 2020 
Drew P. Kouri, Sandia National Laboratories Randomized Sketching for LowMemory Dynamic Optimization ... more ... less 

Abstract: In this talk, we develop a novel limitedmemory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective or cost functional. While the control is often low dimensional, the state is typically more expensive to store. To reduce the memory requirements, we employ randomized matrix approximation to compress the state as it is generated. The compressed state is then used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the compressed state. This approximate first and secondorder information can readily be used in any optimization algorithm. As an example, we develop a sketched trustregion method that adaptively learns the target rank using a posteriori error information and provably converges to a stationary point of the original problem. To conclude, we apply our randomized compression to the optimal control of a linear elliptic PDE and the optimal control of fluid flow past a cylinder. 

Abstract: In this talk, we develop a novel limitedmemory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective or cost functional. While the control is often low dimensional, the state is typically more expensive to store. To reduce the memory requirements, we employ randomized matrix approximation to compress the state as it is generated. The compressed state is then used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the compressed state. This approximate first and secondorder information can readily be used in any optimization algorithm. As an example, we develop a sketched trustregion method that adaptively learns the target rank using a posteriori error information and provably converges to a stationary point of the original problem. To conclude, we apply our randomized compression to the optimal control of a linear elliptic PDE and the optimal control of fluid flow past a cylinder. 

Friday, October 09, 2020 
Kevin Carlberg  Nonlinear model reduction: using machine learning to enable rapid simulation of extremescale physics models ... more ... less  
Fri, Oct 09, 2020 
Kevin Carlberg, Nonlinear model reduction: using machine learning to enable rapid simulation of extremescale physics models ... more ... less 

Abstract: Physicsbased modeling and simulation has become indispensable across many applications in science and engineering, ranging from autonomousvehicle control to designing new materials. However, achieving high predictive fidelity necessitates modeling fine spatiotemporal resolution, which can lead to extremescale computational models whose simulations consume months on thousands of computing cores. This constitutes a formidable computational barrier: the cost of truly highfidelity simulations renders them impractical for important timecritical applications (e.g., rapid design, control, realtime simulation) in engineering and science. In this talk, I will present several advances in the field of nonlinear model reduction that leverage machinelearning techniques ranging from convolutional autoencoders to LSTM networks to overcome this barrier. In particular, these methods produce lowdimensional counterparts to highfidelity models called reducedorder models (ROMs) that exhibit 1) accuracy, 2) low cost, 3) physicalproperty preservation, 4) guaranteed generalization performance, and 5) error quantification. 

Abstract: Physicsbased modeling and simulation has become indispensable across many applications in science and engineering, ranging from autonomousvehicle control to designing new materials. However, achieving high predictive fidelity necessitates modeling fine spatiotemporal resolution, which can lead to extremescale computational models whose simulations consume months on thousands of computing cores. This constitutes a formidable computational barrier: the cost of truly highfidelity simulations renders them impractical for important timecritical applications (e.g., rapid design, control, realtime simulation) in engineering and science. In this talk, I will present several advances in the field of nonlinear model reduction that leverage machinelearning techniques ranging from convolutional autoencoders to LSTM networks to overcome this barrier. In particular, these methods produce lowdimensional counterparts to highfidelity models called reducedorder models (ROMs) that exhibit 1) accuracy, 2) low cost, 3) physicalproperty preservation, 4) guaranteed generalization performance, and 5) error quantification. 

Friday, October 16, 2020 
Noemi Petra  University of California, Merced  Optimal design of largescale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don't know ... more ... less  
Fri, Oct 16, 2020 
Noemi Petra, University of California, Merced Optimal design of largescale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don't know ... more ... less 

Abstract: Optimal experimental design (OED) refers to the task of determining an experimental setup such that the measurements are most informative about the underlying parameters. This is particularly important in situations where experiments are costly or timeconsuming, and thus only a small number of measurements can be collected. In addition to the parameters estimated by an inverse problem, the governing mathematical models often involve simplifications, approximations, or modeling assumptions, resulting in additional uncertainty. These additional uncertainties must be taken into account in the experimental design process; failing to do so could result in suboptimal designs. In this talk, we consider optimal design of infinitedimensional Bayesian linear inverse problems governed by uncertain forward models. In particular, we seek experimental designs that minimize the posterior uncertainty in the primary parameters, while accounting for the uncertainty in secondary (nuisance) parameters. We accomplish this by deriving a marginalized Aoptimality criterion and developing an efficient computational approach for its optimization. We illustrate our approach for estimating an uncertain timedependent source in a contaminant transport model with an uncertain initial state as secondary uncertainty. Our results indicate that accounting for additional model uncertainty in the experimental design process is crucial. References: This presentation is based on the following paper https://arxiv.org/abs/1308.4084 and manuscript https://arxiv.org/abs/2006.11939. 

Abstract: Optimal experimental design (OED) refers to the task of determining an experimental setup such that the measurements are most informative about the underlying parameters. This is particularly important in situations where experiments are costly or timeconsuming, and thus only a small number of measurements can be collected. In addition to the parameters estimated by an inverse problem, the governing mathematical models often involve simplifications, approximations, or modeling assumptions, resulting in additional uncertainty. These additional uncertainties must be taken into account in the experimental design process; failing to do so could result in suboptimal designs. In this talk, we consider optimal design of infinitedimensional Bayesian linear inverse problems governed by uncertain forward models. In particular, we seek experimental designs that minimize the posterior uncertainty in the primary parameters, while accounting for the uncertainty in secondary (nuisance) parameters. We accomplish this by deriving a marginalized Aoptimality criterion and developing an efficient computational approach for its optimization. We illustrate our approach for estimating an uncertain timedependent source in a contaminant transport model with an uncertain initial state as secondary uncertainty. Our results indicate that accounting for additional model uncertainty in the experimental design process is crucial. References: This presentation is based on the following paper https://arxiv.org/abs/1308.4084 and manuscript https://arxiv.org/abs/2006.11939. 

Friday, October 23, 2020 
Boyan Lazarov  Lawrence Livermore National Laboratory  Largescale topology optimization ... more ... less  
Fri, Oct 23, 2020 
Boyan Lazarov, Lawrence Livermore National Laboratory Largescale topology optimization ... more ... less 

Abstract: Topology optimization has gained the status of being the preferred optimization tool in the mechanical, automotive, and aerospace industries. It has undergone tremendous development since its introduction in 1988, and nowadays, it has spread to many other disciplines such as Acoustics, Optics, and Material Design. The basic idea is to distribute material in a predefined domain by minimizing a selected objective and fulfilling a set of constraints. The procedure consists of repeated system analyses, gradient evaluation steps by adjoint sensitivity analysis, and design updates based on mathematical programming methods. Regularization techniques ensure the existence of a solution. The result of the topology optimization procedure is a bitmap image of the design. The ability of the method to modify every pixel/voxel results in design freedom unavailable by any other alternative approach. However, this freedom comes with the requirement of using the computational power of large parallel machines. Incorporating a model accounting for exploitation and manufacturing variations in the optimization process and the high contrast between the material phases increase further the computational cost. Thus, this talk focuses on methods for reducing the computational complexity, ensuring manufacturability of the optimized design and efficient handling of the high contrast of the material properties. The development will be demonstrated in airplane wing design, compliant mechanisms, heat sinks, material microstructures for additive manufacturing, and photonic devices. 

Abstract: Topology optimization has gained the status of being the preferred optimization tool in the mechanical, automotive, and aerospace industries. It has undergone tremendous development since its introduction in 1988, and nowadays, it has spread to many other disciplines such as Acoustics, Optics, and Material Design. The basic idea is to distribute material in a predefined domain by minimizing a selected objective and fulfilling a set of constraints. The procedure consists of repeated system analyses, gradient evaluation steps by adjoint sensitivity analysis, and design updates based on mathematical programming methods. Regularization techniques ensure the existence of a solution. The result of the topology optimization procedure is a bitmap image of the design. The ability of the method to modify every pixel/voxel results in design freedom unavailable by any other alternative approach. However, this freedom comes with the requirement of using the computational power of large parallel machines. Incorporating a model accounting for exploitation and manufacturing variations in the optimization process and the high contrast between the material phases increase further the computational cost. Thus, this talk focuses on methods for reducing the computational complexity, ensuring manufacturability of the optimized design and efficient handling of the high contrast of the material properties. The development will be demonstrated in airplane wing design, compliant mechanisms, heat sinks, material microstructures for additive manufacturing, and photonic devices. 

Friday, October 30, 2020 
Martin J. Gander  University of Geneva  Seven Things I would have liked to know when starting to work on Domain Decomposition ... more ... less  
Fri, Oct 30, 2020 
Martin J. Gander, University of Geneva Seven Things I would have liked to know when starting to work on Domain Decomposition ... more ... less 

Abstract: It is not easy to start working in a new field of research. I will give a personal overview over seven things I would have liked to know when I started working on domain decomposition (DD) methods:


Abstract: It is not easy to start working in a new field of research. I will give a personal overview over seven things I would have liked to know when I started working on domain decomposition (DD) methods:


Friday, November 06, 2020 
Siddhartha Mishra  ETH Zürich  Deep Learning and Computations of PDEs ... more ... less  
Fri, Nov 06, 2020 
Siddhartha Mishra, ETH Zürich Deep Learning and Computations of PDEs ... more ... less 

Abstract: We present recent results on the use of deep learning techniques in the context of computing different aspects of PDEs. The first part of the talk will be on novel supervised learning algorithms for efficient computation of parametric PDEs with applications to Uncertainty quantification and PDE constrained optimization. The second part of the talk will be focussed on a recently proposed class of unsupervised learning algorithms, Physics Informed Neural Networks (PINNs) and we describe their application to compute solutions for the forward problem for highdimensional PDE as well as for the data assimilation inverse problems for PDEs. 

Abstract: We present recent results on the use of deep learning techniques in the context of computing different aspects of PDEs. The first part of the talk will be on novel supervised learning algorithms for efficient computation of parametric PDEs with applications to Uncertainty quantification and PDE constrained optimization. The second part of the talk will be focussed on a recently proposed class of unsupervised learning algorithms, Physics Informed Neural Networks (PINNs) and we describe their application to compute solutions for the forward problem for highdimensional PDE as well as for the data assimilation inverse problems for PDEs. 

Friday, November 13, 2020 
Jianfeng Lu  Duke University  Solving Eigenvalue Problems in High Dimension ... more ... less  
Fri, Nov 13, 2020 
Jianfeng Lu, Duke University Solving Eigenvalue Problems in High Dimension ... more ... less 

Abstract: The leading eigenvalue problem of a differential operator arises in many scientific and engineering applications, in particular quantum manybody problems. Due to the curse of dimensionality, conventional algorithms become impractical due to the huge computational and memory complexity. In this talk, we will discuss some of our recent works on novel approaches for eigenvalue problems in high dimension, using techniques from randomized algorithms, coordinate methods, and deep learning. (joint work with Jiequn Han, Yingzhou Li, Zhe Wang and Mo Zhou). 

Abstract: The leading eigenvalue problem of a differential operator arises in many scientific and engineering applications, in particular quantum manybody problems. Due to the curse of dimensionality, conventional algorithms become impractical due to the huge computational and memory complexity. In this talk, we will discuss some of our recent works on novel approaches for eigenvalue problems in high dimension, using techniques from randomized algorithms, coordinate methods, and deep learning. (joint work with Jiequn Han, Yingzhou Li, Zhe Wang and Mo Zhou). 

Friday, November 20, 2020 
Ramnarayan Krishnamurthy  MathWorks  HandsOn Workshop  Deep Learning in MATLAB ... more ... less  
Fri, Nov 20, 2020 
Ramnarayan Krishnamurthy, MathWorks HandsOn Workshop  Deep Learning in MATLAB ... more ... less 

Abstract: Artificial Intelligence techniques like deep learning are introducing automation to the products we build and the way we do business. These techniques can be used to solve complex problems related to images, signals, text and controls. In this handson workshop, you will write code and use MATLAB Online to:
Follow up: Useful Resources


Abstract: Artificial Intelligence techniques like deep learning are introducing automation to the products we build and the way we do business. These techniques can be used to solve complex problems related to images, signals, text and controls. In this handson workshop, you will write code and use MATLAB Online to:
Follow up: Useful Resources


Friday, November 27, 2020 
Thanksgiving Break  
Fri, Nov 27, 2020 
Thanksgiving Break


Friday, December 04, 2020 
Rayanne Luke  University of Delaware  Parameter Identification for Tear Film Thinning and Breakup ... more ... less  
Fri, Dec 04, 2020 
Rayanne Luke, University of Delaware Parameter Identification for Tear Film Thinning and Breakup ... more ... less 

Abstract: Millions of Americans experience dry eye syndrome, a condition that decreases quality of vision and causes ocular discomfort. A phenomenon associated with dry eye syndrome is tear film breakup (TBU), or the formation of dry spots on the eye. The dynamics of the tear film can be studied using fluorescence imaging. Many parameters affecting tear film thickness and fluorescent intensity distributions within TBU are difficult to measure directly in vivo. We estimate breakup parameters by fitting computed results from thin film fluid PDE models to experimental fluorescent intensity data gathered from normal subjects’ tear films in vivo. Both evaporation and the Marangoni effect can cause breakup. The PDE models include these mechanisms in combination and separately. The parameters are determined by a nonlinear least squares minimization between computed and experimental fluorescent intensity, and they indicate the relative importance of each mechanism. Optimal values for computed breakup variables that cannot be measured in vivo fall near or within accepted experimental ranges for the general corneal region. Our results are a step towards characterizing the mechanisms that cause a wide range of breakup instances and help medical professionals to better understand tear film function and dry eye syndrome. 

Abstract: Millions of Americans experience dry eye syndrome, a condition that decreases quality of vision and causes ocular discomfort. A phenomenon associated with dry eye syndrome is tear film breakup (TBU), or the formation of dry spots on the eye. The dynamics of the tear film can be studied using fluorescence imaging. Many parameters affecting tear film thickness and fluorescent intensity distributions within TBU are difficult to measure directly in vivo. We estimate breakup parameters by fitting computed results from thin film fluid PDE models to experimental fluorescent intensity data gathered from normal subjects’ tear films in vivo. Both evaporation and the Marangoni effect can cause breakup. The PDE models include these mechanisms in combination and separately. The parameters are determined by a nonlinear least squares minimization between computed and experimental fluorescent intensity, and they indicate the relative importance of each mechanism. Optimal values for computed breakup variables that cannot be measured in vivo fall near or within accepted experimental ranges for the general corneal region. Our results are a step towards characterizing the mechanisms that cause a wide range of breakup instances and help medical professionals to better understand tear film function and dry eye syndrome. 

Stephan Wojtowytsch  Princeton University  Tetrahedral symmetry in the final and penultimate layers of neural network classifiers ... more ... less  
Stephan Wojtowytsch, Princeton University Tetrahedral symmetry in the final and penultimate layers of neural network classifiers ... more ... less 

Abstract: A recent empirical study found that the penultimate layer of a welltrained neural network classifier maps training data samples to the vertices of a lowdimensional tetrahedron in a highdimensional ambient space. We explain this observation from a theoretical perspective in a toy model for deep networks and give complementary examples to show that even the output of a shallow neural network classifier is generally nonuniform over a data class. As deep networks are the composition of a (slightly less) deep network and a shallow network, these example illustrate how a network would fail to output a uniform classifier over the training samples if the data is mapped to sets with inconvenient geometry in an intermediate layer. 

Abstract: A recent empirical study found that the penultimate layer of a welltrained neural network classifier maps training data samples to the vertices of a lowdimensional tetrahedron in a highdimensional ambient space. We explain this observation from a theoretical perspective in a toy model for deep networks and give complementary examples to show that even the output of a shallow neural network classifier is generally nonuniform over a data class. As deep networks are the composition of a (slightly less) deep network and a shallow network, these example illustrate how a network would fail to output a uniform classifier over the training samples if the data is mapped to sets with inconvenient geometry in an intermediate layer. 
Summer 2020 ... hide
Date  Speaker  Affiliation  Title  

Date  Speaker, Affiliation, Title  
Friday, May 22, 2020 
Jianghao Wang  MathWorks  Practical Deep Learning in the Classroom ... more ... less  
Fri, May 22, 2020 
Jianghao Wang, MathWorks Practical Deep Learning in the Classroom ... more ... less 

Abstract: Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students to adopt this technology, almost regardless of what their future jobs are. We will highlight some of the mathematics needed to construct and understand deep learning solutions. About the speaker:Jianghao Wang is the deep learning academic liaison at MathWorks. In her role, Jianghao supports deep learning research and teaching in academia. Before joining MathWorks, Jianghao obtained her Ph.D. in Statistical Climatology from the University of Southern California and B.S. in Applied Mathematics from Nankai University. 

Abstract: Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students to adopt this technology, almost regardless of what their future jobs are. We will highlight some of the mathematics needed to construct and understand deep learning solutions. About the speaker:Jianghao Wang is the deep learning academic liaison at MathWorks. In her role, Jianghao supports deep learning research and teaching in academia. Before joining MathWorks, Jianghao obtained her Ph.D. in Statistical Climatology from the University of Southern California and B.S. in Applied Mathematics from Nankai University. 

Friday, May 29, 2020 
Akwum Onwunta  University of Maryland, College Park  Fast solvers for optimal control problems constrained by PDEs with uncertain inputs ... more ... less  
Fri, May 29, 2020 
Akwum Onwunta, University of Maryland, College Park Fast solvers for optimal control problems constrained by PDEs with uncertain inputs ... more ... less 

Abstract: Optimization problems constrained by deterministic steadystate partial differential equations (PDEs) are computationally challenging. This is even more so if the constraints are deterministic unsteady PDEs since one would then need to solve a system of PDEs coupled globally in time and space, and timestepping methods quickly reach their limitations due to the enormous demand for storage [5]. Yet, more challenging than the aforementioned are problems constrained by unsteady PDEs involving (countably many) parametric or uncertain inputs. A viable solution approach to optimization problems with stochastic constraints employs the spectral stochastic Galerkin finite element method (SGFEM). However, the SGFEM often leads to the socalled curse of dimensionality, in the sense that it results in prohibitively high dimensional linear systems with tensor product structure [1, 2, 4]. Moreover, a typical model for an optimal control problem with stochastic inputs (OCPS) will usually be used for the quantification of the statistics of the system response – a task that could in turn result in additional enormous computational expense. It is worth pursuing computationally efficient ways to simulate OCPS using SGFEMs since the Galerkin approximation provides a favorable framework for error estimation [3]. In this talk, we consider two prototypical model OCPS and discretize them with SGFEM. We exploit the underlying mathematical structure of the discretized systems at the heart of the optimization routine to derive and analyze low rank iterative solvers and robust blockdiagonal preconditioners for solving the resulting stochastic Galerkin systems. The developed solvers are quite efficient in the reduction of temporal and storage requirements of the highdimensional linear systems [1, 2]. Finally, we illustrate the effectiveness of our solvers with numerical experiments. Keywords: Stochastic Galerkin system, iterative methods, PDEconstrained optimization, saddlepoint system, lowrank solution, preconditioning, Schur complement. References:
Akwum Onwunta is a postdoctoral research associate at the University of Maryland, College Park (UMCP). Before joining UMCP, he had worked at Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany as a scientific researcher and at Deutsche Bank, Frankfurt, as a Marie Curie research fellow / quantitative risk analyst. He holds a PhD in Mathematics from Otto von Guericke University, Magdeburg, Germany. 

Abstract: Optimization problems constrained by deterministic steadystate partial differential equations (PDEs) are computationally challenging. This is even more so if the constraints are deterministic unsteady PDEs since one would then need to solve a system of PDEs coupled globally in time and space, and timestepping methods quickly reach their limitations due to the enormous demand for storage [5]. Yet, more challenging than the aforementioned are problems constrained by unsteady PDEs involving (countably many) parametric or uncertain inputs. A viable solution approach to optimization problems with stochastic constraints employs the spectral stochastic Galerkin finite element method (SGFEM). However, the SGFEM often leads to the socalled curse of dimensionality, in the sense that it results in prohibitively high dimensional linear systems with tensor product structure [1, 2, 4]. Moreover, a typical model for an optimal control problem with stochastic inputs (OCPS) will usually be used for the quantification of the statistics of the system response – a task that could in turn result in additional enormous computational expense. It is worth pursuing computationally efficient ways to simulate OCPS using SGFEMs since the Galerkin approximation provides a favorable framework for error estimation [3]. In this talk, we consider two prototypical model OCPS and discretize them with SGFEM. We exploit the underlying mathematical structure of the discretized systems at the heart of the optimization routine to derive and analyze low rank iterative solvers and robust blockdiagonal preconditioners for solving the resulting stochastic Galerkin systems. The developed solvers are quite efficient in the reduction of temporal and storage requirements of the highdimensional linear systems [1, 2]. Finally, we illustrate the effectiveness of our solvers with numerical experiments. Keywords: Stochastic Galerkin system, iterative methods, PDEconstrained optimization, saddlepoint system, lowrank solution, preconditioning, Schur complement. References:
Akwum Onwunta is a postdoctoral research associate at the University of Maryland, College Park (UMCP). Before joining UMCP, he had worked at Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany as a scientific researcher and at Deutsche Bank, Frankfurt, as a Marie Curie research fellow / quantitative risk analyst. He holds a PhD in Mathematics from Otto von Guericke University, Magdeburg, Germany. 

Friday, June 05, 2020 
Patrick O’Neil  BlackSky  Applications of Deep Learning to Large Scale Remote Sensing ... more ... less  
Fri, Jun 05, 2020 
Patrick O’Neil, BlackSky Applications of Deep Learning to Large Scale Remote Sensing ... more ... less 

Abstract: With the proliferation of Earth imaging satellites, the rate at which satellite imagery is acquired has outpaced the ability to manually review the data. Therefore, it is critical to develop systems capable of autonomously monitoring the globe for change. At BlackSky, we use a host of deep learning models, deployed in Amazon Web Services, to process all images downlinked from our Globals constellation of imaging satellites. In this talk, we will discuss some of these models and challenges we face when building remote sensing machine learning models at scale. 

Abstract: With the proliferation of Earth imaging satellites, the rate at which satellite imagery is acquired has outpaced the ability to manually review the data. Therefore, it is critical to develop systems capable of autonomously monitoring the globe for change. At BlackSky, we use a host of deep learning models, deployed in Amazon Web Services, to process all images downlinked from our Globals constellation of imaging satellites. In this talk, we will discuss some of these models and challenges we face when building remote sensing machine learning models at scale. 

Friday, June 12, 2020 
Ira B. Schwartz  US Naval Research Laboratory  Fear in Networks: How social adaptation controls epidemic outbreaks ... more ... less  
Fri, Jun 12, 2020 
Ira B. Schwartz, US Naval Research Laboratory Fear in Networks: How social adaptation controls epidemic outbreaks ... more ... less 

Abstract: Disease control is of paramount importance in public health, with total eradication as the ultimate goal. Mathematical models of disease spread in populations are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak of disease has only recently been included in the modeling of epidemics on networks. In this talk, I will review some of the mathematical models and machinery used to describe the underlying dynamics of rare events in finite population disease models, which include human reactions on what are called adaptive networks. A new model that includes a dynamical systems description of the force of the noise that drives the disease to extinction. Coupling the effective force of noise with vaccination as well as human behavior reveals how to best utilize stochastic disease controlling resources such as vaccination and treatment programs. Finally, I will also present a general theory to derive the most probable paths to extinction for heterogeneous networks, which leads to a novel optimal control to extinction. This research has been supported by the Office of Naval Research, Air Force of Scientific Research and the National Institutes of Health, and done primarily in collaboration with Jason Hindes, Brandon Lindley, and Leah Shaw. About the speaker:Trained and educated as both an applied mathematician (University of Marylan, Ph.D.) and physicist (University of Hartford, BS), Dr. Schwartz and his collaborators, post doctoral fellows and students have impacted a diverse array of applications in the field of nonlinear science. Dr. Schwartz has over 120 refereed publications in areas such as physics, mathematics, biology and chemistry. The main underlying theme in the applications field has been the mathematical and numerical techniques of nonlinear dynamics and chaos, and most recently, nonlinear stochastic analysis and control of cooperative and networked dynamical systems. Dr. Schwartz has been written up several times in Science and Scientific American magazines, has given invited and plenary talks at international applied mathematics, physics, and engineering conferences, and he is one of the founding organizers of the biennial SIAM conference on Dynamical Systems. Several of his discoveries developed in nonlinear science are currently patented, including collaborative robots, synchronized coupled lasers, and chaos tracking and control for which he was awarded the US Navy Tech Transfer award. Dr. Schwartz is an elected fellow of the American Physical Society and the current vicechair of the SIAM Dynamical Systems Group. 

Abstract: Disease control is of paramount importance in public health, with total eradication as the ultimate goal. Mathematical models of disease spread in populations are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak of disease has only recently been included in the modeling of epidemics on networks. In this talk, I will review some of the mathematical models and machinery used to describe the underlying dynamics of rare events in finite population disease models, which include human reactions on what are called adaptive networks. A new model that includes a dynamical systems description of the force of the noise that drives the disease to extinction. Coupling the effective force of noise with vaccination as well as human behavior reveals how to best utilize stochastic disease controlling resources such as vaccination and treatment programs. Finally, I will also present a general theory to derive the most probable paths to extinction for heterogeneous networks, which leads to a novel optimal control to extinction. This research has been supported by the Office of Naval Research, Air Force of Scientific Research and the National Institutes of Health, and done primarily in collaboration with Jason Hindes, Brandon Lindley, and Leah Shaw. About the speaker:Trained and educated as both an applied mathematician (University of Marylan, Ph.D.) and physicist (University of Hartford, BS), Dr. Schwartz and his collaborators, post doctoral fellows and students have impacted a diverse array of applications in the field of nonlinear science. Dr. Schwartz has over 120 refereed publications in areas such as physics, mathematics, biology and chemistry. The main underlying theme in the applications field has been the mathematical and numerical techniques of nonlinear dynamics and chaos, and most recently, nonlinear stochastic analysis and control of cooperative and networked dynamical systems. Dr. Schwartz has been written up several times in Science and Scientific American magazines, has given invited and plenary talks at international applied mathematics, physics, and engineering conferences, and he is one of the founding organizers of the biennial SIAM conference on Dynamical Systems. Several of his discoveries developed in nonlinear science are currently patented, including collaborative robots, synchronized coupled lasers, and chaos tracking and control for which he was awarded the US Navy Tech Transfer award. Dr. Schwartz is an elected fellow of the American Physical Society and the current vicechair of the SIAM Dynamical Systems Group. 

Friday, June 19, 2020 
Thomas M. Surowiec  PhilippsUniversität Marburg  Optimization of Elliptic PDEs with Uncertain Inputs: Basic Theory and Numerical Stability ... more ... less  
Fri, Jun 19, 2020 
Thomas M. Surowiec, PhilippsUniversität Marburg Optimization of Elliptic PDEs with Uncertain Inputs: Basic Theory and Numerical Stability ... more ... less 

Abstract: Systems of partial differential equations subject to random parameters provide a natural way of incorporating noisy data or model uncertainty into a mathematical setting. The associated optimal decisionmaking problems, whose feasible sets are at least partially governed by the solutions of these random PDEs, are infinite dimensional stochastic optimization problems. In order to obtain solutions that are resilient to the underlying uncertainty, a common approach is to use risk measures to model the user’s risk preference. The talk will be split into two main parts: Basic Theory and Numerical Stability. In the first part, we propose a minimal set of technical assumptions needed to prove existence of solutions and derive optimality conditions. For the second part of the talk, we consider a specific class of stochastic optimization problems motivated by the application to PDEconstrained optimization. In particular, we are interested in finding answers to such questions as: How do the solutions behave in the largedata limit? Can we derive statements on the rate of convergence as the samplesize increases and meshsize decreases? After reviewing several notions of probability metrics and their usage in stability analysis of stochastic optimization problems, we present qualitative and quantitative stability results. These results demonstrate the parametric dependence of the optimal values and optimal solutions with respect to changes in the underlying probability measure. These statements provide us with answers to the questions posed above for a class of riskneutral PDEconstrained problems. 

Abstract: Systems of partial differential equations subject to random parameters provide a natural way of incorporating noisy data or model uncertainty into a mathematical setting. The associated optimal decisionmaking problems, whose feasible sets are at least partially governed by the solutions of these random PDEs, are infinite dimensional stochastic optimization problems. In order to obtain solutions that are resilient to the underlying uncertainty, a common approach is to use risk measures to model the user’s risk preference. The talk will be split into two main parts: Basic Theory and Numerical Stability. In the first part, we propose a minimal set of technical assumptions needed to prove existence of solutions and derive optimality conditions. For the second part of the talk, we consider a specific class of stochastic optimization problems motivated by the application to PDEconstrained optimization. In particular, we are interested in finding answers to such questions as: How do the solutions behave in the largedata limit? Can we derive statements on the rate of convergence as the samplesize increases and meshsize decreases? After reviewing several notions of probability metrics and their usage in stability analysis of stochastic optimization problems, we present qualitative and quantitative stability results. These results demonstrate the parametric dependence of the optimal values and optimal solutions with respect to changes in the underlying probability measure. These statements provide us with answers to the questions posed above for a class of riskneutral PDEconstrained problems. 

Friday, June 26, 2020 
Mahamadi Warma  George Mason University  Fractional PDEs and their controllability properties: What is so far known and what is still unknown? ... more ... less  
Fri, Jun 26, 2020 
Mahamadi Warma, George Mason University Fractional PDEs and their controllability properties: What is so far known and what is still unknown? ... more ... less 

Abstract: In this talk, we are interested to fractional PDEs (elliptic, parabolic and hyperbolic) associated with the fractional Laplace operator. After introducing some reallife phenomena where these problems occur, we shall give a complete overview on the subject. The similarities and the differences of these fractional PDEs with the classical local PDEs with be discussed. Concerning the control theory of fractional PDEs, we will give a complete overview of the topic. More precisely, we will introduce the known important results so far obtained and we will enumerate several related important problems that have been not yet investigated by the Mathematics community. The talk will be delivered for a wide audience avoiding unnecessary technicalities. 

Abstract: In this talk, we are interested to fractional PDEs (elliptic, parabolic and hyperbolic) associated with the fractional Laplace operator. After introducing some reallife phenomena where these problems occur, we shall give a complete overview on the subject. The similarities and the differences of these fractional PDEs with the classical local PDEs with be discussed. Concerning the control theory of fractional PDEs, we will give a complete overview of the topic. More precisely, we will introduce the known important results so far obtained and we will enumerate several related important problems that have been not yet investigated by the Mathematics community. The talk will be delivered for a wide audience avoiding unnecessary technicalities. 

Friday, July 03, 2020 
no colloquium  
Fri, Jul 03, 2020 
no colloquium  
Friday, July 10, 2020 
John Harlim  The Pennsylvania State University  Learning Missing Dynamics through Data ... more ... less  (password: 7v.#=9%N) 
Fri, Jul 10, 2020 
John Harlim, The Pennsylvania State University Learning Missing Dynamics through Data ... more ... less 

Abstract: Recent success of machine learning has drawn tremendous interest in applied mathematics and scientific computations. In this talk, I would address the classical closure problem that is also known as model error, missing dynamics, or reducedordermodeling in various community. Particularly, I will discuss a general framework to compensate for the model error. The proposed framework reformulates the model error problem into a supervised learning task to approximate very highdimensional target functions, involving the MoriZwanzig representation of the projected dynamical systems. Connection to traditional parametric approaches will be clarified as specifying the appropriate hypothesis space for the target function. Theoretical convergence and numerical demonstration on modeling problems arising from PDE's will be discussed. 

Abstract: Recent success of machine learning has drawn tremendous interest in applied mathematics and scientific computations. In this talk, I would address the classical closure problem that is also known as model error, missing dynamics, or reducedordermodeling in various community. Particularly, I will discuss a general framework to compensate for the model error. The proposed framework reformulates the model error problem into a supervised learning task to approximate very highdimensional target functions, involving the MoriZwanzig representation of the projected dynamical systems. Connection to traditional parametric approaches will be clarified as specifying the appropriate hypothesis space for the target function. Theoretical convergence and numerical demonstration on modeling problems arising from PDE's will be discussed. 

Friday, July 17, 2020 
Maziar Raissi  University of Colorado Boulder  Hidden Physics Models ... more ... less  (password: 1P&@+!5v) 
Fri, Jul 17, 2020 
Maziar Raissi, University of Colorado Boulder Hidden Physics Models ... more ... less 

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the longstanding developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing dataefficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear differential equations, to extract patterns from highdimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multifidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations. 

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the longstanding developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing dataefficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear differential equations, to extract patterns from highdimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multifidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations. 

Friday, July 24, 2020 
Ratna Khatri  Naval Research Lab  Fractional Deep Neural Network via Constrained Optimization ... more ... less  
Fri, Jul 24, 2020 
Ratna Khatri, Naval Research Lab Fractional Deep Neural Network via Constrained Optimization ... more ... less 

Abstract: In this talk, we will introduce a novel algorithmic framework for a deep neural network (DNN) which allows us to incorporate history (or memory) into the network. This DNN, called FractionalDNN, can be viewed as a timediscretization of a fractional in time nonlinear ordinary differential equation (ODE). The learning problem then is a minimization problem subject to that fractional ODE as constraints. We test our network on datasets for classification problems. The key advantages of the fractionalDNN are a significant improvement to the vanishing gradient issue due to the memory effect, and a better handling of nonsmooth data due to the network's ability to approximate nonsmooth functions. 

Abstract: In this talk, we will introduce a novel algorithmic framework for a deep neural network (DNN) which allows us to incorporate history (or memory) into the network. This DNN, called FractionalDNN, can be viewed as a timediscretization of a fractional in time nonlinear ordinary differential equation (ODE). The learning problem then is a minimization problem subject to that fractional ODE as constraints. We test our network on datasets for classification problems. The key advantages of the fractionalDNN are a significant improvement to the vanishing gradient issue due to the memory effect, and a better handling of nonsmooth data due to the network's ability to approximate nonsmooth functions. 

Birgul Koc  Virginia Tech  DataDriven Variational Multiscale Reduced Order Models ... more ... less  
Birgul Koc, Virginia Tech DataDriven Variational Multiscale Reduced Order Models ... more ... less 

Abstract: We propose a new datadriven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMSROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, instead of ad hoc modeling techniques used in VMS for standard numerical methods (e.g., finite element), we use available data to model the VMSROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMSROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new datadriven VMSROM in the numerical simulation of the 1D Burgers equation and the 2D flow past a circular cylinder. The numerical results show that the datadriven VMSROM is significantly more accurate than standard ROMs. 

Abstract: We propose a new datadriven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMSROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, instead of ad hoc modeling techniques used in VMS for standard numerical methods (e.g., finite element), we use available data to model the VMSROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMSROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new datadriven VMSROM in the numerical simulation of the 1D Burgers equation and the 2D flow past a circular cylinder. The numerical results show that the datadriven VMSROM is significantly more accurate than standard ROMs. 

Friday, July 31, 2020 
Eric Cyr  Sandia National Laboratories  A LayerParallel Approach for Training Deep Neural Networks ... more ... less  
Fri, Jul 31, 2020 
Eric Cyr, Sandia National Laboratories A LayerParallel Approach for Training Deep Neural Networks ... more ... less 

Abstract: Deep neural networks are a powerful machine learning tool with the capacity to “learn” complex nonlinear relationships described by large data sets. Despite their success training these models remains a challenging and computationally intensive undertaking. In this talk we will present a new layerparallel training algorithm that exploits a multigrid scheme to accelerate both forward and backward propagation. Introducing a parallel decomposition between layers requires inexact propagation of the neural network. The multigrid method used in this approach stiches these subdomains together with sufficient accuracy to ensure rapid convergence. We demonstrate an order of magnitude wallclock time speedup over the serial approach, opening a new avenue for parallelism that is complementary to existing approaches. Results for this talk can be found in [1,2]. We will also present related work concerning parallelintime optimization algorithms for PDEconstrained optimization. [1] S. Guenther, L. Ruthotto, J. B. Schroder, E. C. Cyr, N. R. Gauger, LayerParallel Training of Deep Residual Neural Networks, SIMODs, Vol. 2 (1), 2020. 

Abstract: Deep neural networks are a powerful machine learning tool with the capacity to “learn” complex nonlinear relationships described by large data sets. Despite their success training these models remains a challenging and computationally intensive undertaking. In this talk we will present a new layerparallel training algorithm that exploits a multigrid scheme to accelerate both forward and backward propagation. Introducing a parallel decomposition between layers requires inexact propagation of the neural network. The multigrid method used in this approach stiches these subdomains together with sufficient accuracy to ensure rapid convergence. We demonstrate an order of magnitude wallclock time speedup over the serial approach, opening a new avenue for parallelism that is complementary to existing approaches. Results for this talk can be found in [1,2]. We will also present related work concerning parallelintime optimization algorithms for PDEconstrained optimization. [1] S. Guenther, L. Ruthotto, J. B. Schroder, E. C. Cyr, N. R. Gauger, LayerParallel Training of Deep Residual Neural Networks, SIMODs, Vol. 2 (1), 2020. 

Friday, August 07, 2020 
Marta D'Elia  Sandia National Laboratories  A unified theoretical and computational nonlocal framework: generalized vector calculus and machinelearned nonlocal models ... more ... less  
Fri, Aug 07, 2020 
Marta D'Elia, Sandia National Laboratories A unified theoretical and computational nonlocal framework: generalized vector calculus and machinelearned nonlocal models ... more ... less 

Abstract: Nonlocal models provide an improved predictive capability thanks to their ability to capture effects that classical partial differential equations fail to capture. Among these effects we have multiscale behavior (e.g. in fracture mechanics) and anomalous behavior such as super and subdiffusion. These models have become incredibly popular for a broad range of applications, including mechanics, subsurface flow, turbulence, heat conduction and image processing. However, their improved accuracy comes at a price of many modeling and numerical challenges. In this talk I will first address the problem of connecting nonlocal and fractional calculus by developing a unified theoretical framework that enables the identification of a broad class of nonlocal models. Then, I will present two recently developed machinelearning techniques for nonlocal and fractional model learning. These physicsinformed, datadriven, tools allow for the reconstruction of model parameters or nonlocal kernels. Several numerical tests in one and two dimensions illustrate our theoretical findings and the robustness and accuracy of our approaches. 

Abstract: Nonlocal models provide an improved predictive capability thanks to their ability to capture effects that classical partial differential equations fail to capture. Among these effects we have multiscale behavior (e.g. in fracture mechanics) and anomalous behavior such as super and subdiffusion. These models have become incredibly popular for a broad range of applications, including mechanics, subsurface flow, turbulence, heat conduction and image processing. However, their improved accuracy comes at a price of many modeling and numerical challenges. In this talk I will first address the problem of connecting nonlocal and fractional calculus by developing a unified theoretical framework that enables the identification of a broad class of nonlocal models. Then, I will present two recently developed machinelearning techniques for nonlocal and fractional model learning. These physicsinformed, datadriven, tools allow for the reconstruction of model parameters or nonlocal kernels. Several numerical tests in one and two dimensions illustrate our theoretical findings and the robustness and accuracy of our approaches. 