Events

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).

Fall 2020

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 ill-posed 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 non-linear 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 two-fold 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 non-self-adjoint) 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 ill-posed 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 non-linear 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 two-fold 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 non-self-adjoint) 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 Oseen-Frank, Landau-de 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 Landau-de 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 co-authors which I will review. I will illustrate the methods we have developed by presenting numerical simulations in two and three dimensions including non-orientable 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 Oseen-Frank, Landau-de 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 Landau-de 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 co-authors which I will review. I will illustrate the methods we have developed by presenting numerical simulations in two and three dimensions including non-orientable 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
Carola-Bibiane Schönlieb University of Cambridge Multi-tasking inverse problems: more together than alone ... more ... less
Fri, Sep 25,
2020
Carola-Bibiane Schönlieb,
University of Cambridge

Multi-tasking 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 multi-tasking 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 all-in-one approach practically possible for the first time.
In this talk we will discuss multi-tasking approaches to inverse problems, and their analytical and numerical challenges. This will include a variational model for joint motion estimation and reconstruction for fast tomographic imaging, joint registration and reconstruction (using a template image as a shape prior in the reconstruction) for limited angle tomography, as well as a variational model for joint image reconstruction and segmentation for MRI. These variational approaches will be put in contrast to a deep learning framework for multi-tasking inverse problems, with examples for joint image reconstruction and segmentation, and joint image reconstruction and classification from tomographic data.

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 multi-tasking 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 all-in-one approach practically possible for the first time.
In this talk we will discuss multi-tasking approaches to inverse problems, and their analytical and numerical challenges. This will include a variational model for joint motion estimation and reconstruction for fast tomographic imaging, joint registration and reconstruction (using a template image as a shape prior in the reconstruction) for limited angle tomography, as well as a variational model for joint image reconstruction and segmentation for MRI. These variational approaches will be put in contrast to a deep learning framework for multi-tasking inverse problems, with examples for joint image reconstruction and segmentation, and joint image reconstruction and classification from tomographic data.

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

Randomized Sketching for Low-Memory Dynamic Optimization ... more ... less


Abstract:

In this talk, we develop a novel limited-memory 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 second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region 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 limited-memory 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 second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region 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 Facebook Nonlinear model reduction: using machine learning to enable rapid simulation of extreme-scale physics models ... more ... less
Fri, Oct 09,
2020
Kevin Carlberg,
Facebook

Nonlinear model reduction: using machine learning to enable rapid simulation of extreme-scale physics models ... more ... less


Abstract:

Physics-based modeling and simulation has become indispensable across many applications in science and engineering, ranging from autonomous-vehicle control to designing new materials. However, achieving high predictive fidelity necessitates modeling fine spatiotemporal resolution, which can lead to extreme-scale computational models whose simulations consume months on thousands of computing cores. This constitutes a formidable computational barrier: the cost of truly high-fidelity simulations renders them impractical for important time-critical applications (e.g., rapid design, control, real-time simulation) in engineering and science. In this talk, I will present several advances in the field of nonlinear model reduction that leverage machine-learning techniques ranging from convolutional autoencoders to LSTM networks to overcome this barrier. In particular, these methods produce low-dimensional counterparts to high-fidelity models called reduced-order models (ROMs) that exhibit 1) accuracy, 2) low cost, 3) physical-property preservation, 4) guaranteed generalization performance, and 5) error quantification.

Abstract:

Physics-based modeling and simulation has become indispensable across many applications in science and engineering, ranging from autonomous-vehicle control to designing new materials. However, achieving high predictive fidelity necessitates modeling fine spatiotemporal resolution, which can lead to extreme-scale computational models whose simulations consume months on thousands of computing cores. This constitutes a formidable computational barrier: the cost of truly high-fidelity simulations renders them impractical for important time-critical applications (e.g., rapid design, control, real-time simulation) in engineering and science. In this talk, I will present several advances in the field of nonlinear model reduction that leverage machine-learning techniques ranging from convolutional autoencoders to LSTM networks to overcome this barrier. In particular, these methods produce low-dimensional counterparts to high-fidelity models called reduced-order models (ROMs) that exhibit 1) accuracy, 2) low cost, 3) physical-property preservation, 4) guaranteed generalization performance, and 5) error quantification.

Friday,
October 16, 2020
Noemi Petra University of California, Merced Optimal design of large-scale 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 large-scale 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 time-consuming, 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 infinite-dimensional 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 A-optimality criterion and developing an efficient computational approach for its optimization. We illustrate our approach for estimating an uncertain time-dependent 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 time-consuming, 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 infinite-dimensional 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 A-optimality criterion and developing an efficient computational approach for its optimization. We illustrate our approach for estimating an uncertain time-dependent 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 Large-scale topology optimization ... more ... less
Fri, Oct 23,
2020
Boyan Lazarov,
Lawrence Livermore National Laboratory

Large-scale 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:

  1. Seminal contributions to DD not easy to start with
  2. Seminal contributions to DD ideal to start with
  3. DD solvers are obtained by discretizing 2)
  4. There are better transmission conditions than Dirichlet or Neumann
  5. "Optimal" in classical DD means scalable
  6. Coarse space components can do more than provide scalability
  7. DD methods should always be used as preconditioners

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:

  1. Seminal contributions to DD not easy to start with
  2. Seminal contributions to DD ideal to start with
  3. DD solvers are obtained by discretizing 2)
  4. There are better transmission conditions than Dirichlet or Neumann
  5. "Optimal" in classical DD means scalable
  6. Coarse space components can do more than provide scalability
  7. DD methods should always be used as preconditioners

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 high-dimensional 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 high-dimensional 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 many-body 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 many-body 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 Hands-On Workshop - Deep Learning in MATLAB ... more ... less
Fri, Nov 20,
2020
Ramnarayan Krishnamurthy,
MathWorks

Hands-On 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 hands-on workshop, you will write code and use MATLAB Online to:

  1. Train deep neural networks on GPUs in the cloud.
  2. Create deep learning models from scratch for image and signal data.
  3. Explore pretrained models and use transfer learning.
  4. Import and export models from Python frameworks such as Keras and PyTorch.
  5. Automatically generate code for embedded targets.

Register here

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 hands-on workshop, you will write code and use MATLAB Online to:

  1. Train deep neural networks on GPUs in the cloud.
  2. Create deep learning models from scratch for image and signal data.
  3. Explore pretrained models and use transfer learning.
  4. Import and export models from Python frameworks such as Keras and PyTorch.
  5. Automatically generate code for embedded targets.

Register here

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
Stephan Wojtowytsch,
Princeton University

Summer 2020

Date Speaker Affiliation Title
Date Speaker, Affiliation, Title
Friday,
August 07, 2020
Marta D'Elia Sandia National Laboratories A unified theoretical and computational nonlocal framework: generalized vector calculus and machine-learned 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 machine-learned 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 sub-diffusion. 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 machine-learning techniques for nonlocal and fractional model learning. These physics-informed, data-driven, 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 sub-diffusion. 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 machine-learning techniques for nonlocal and fractional model learning. These physics-informed, data-driven, 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.

Friday,
July 31, 2020
Eric Cyr Sandia National Laboratories A Layer-Parallel Approach for Training Deep Neural Networks ... more ... less (password: i?1tm&g3)
Fri, Jul 31,
2020
Eric Cyr,
Sandia National Laboratories

A Layer-Parallel Approach for Training Deep Neural Networks ... more ... less


(password: i?1tm&g3)
video
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 layer-parallel 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 wall-clock 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 parallel-in-time optimization algorithms for PDE-constrained optimization.

[1] S. Guenther, L. Ruthotto, J. B. Schroder, E. C. Cyr, N. R. Gauger, Layer-Parallel Training of Deep Residual Neural Networks, SIMODs, Vol. 2 (1), 2020.
[2] E. C. Cyr, S. Guenther, J. B. Schroder, Multilevel Initialization for Layer-Parallel Deep Neural Network Training, arXiv preprint arXiv:1912.08974, 2019.

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 layer-parallel 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 wall-clock 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 parallel-in-time optimization algorithms for PDE-constrained optimization.

[1] S. Guenther, L. Ruthotto, J. B. Schroder, E. C. Cyr, N. R. Gauger, Layer-Parallel Training of Deep Residual Neural Networks, SIMODs, Vol. 2 (1), 2020.
[2] E. C. Cyr, S. Guenther, J. B. Schroder, Multilevel Initialization for Layer-Parallel Deep Neural Network Training, arXiv preprint arXiv:1912.08974, 2019.

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 Fractional-DNN, can be viewed as a time-discretization 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 fractional-DNN 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 non-smooth 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 Fractional-DNN, can be viewed as a time-discretization 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 fractional-DNN 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 non-smooth functions.

Birgul Koc Virginia Tech Data-Driven Variational Multiscale Reduced Order Models ... more ... less
Birgul Koc,
Virginia Tech

Data-Driven Variational Multiscale Reduced Order Models ... more ... less


Abstract:

We propose a new data-driven 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 VMS-ROM 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 VMS-ROM 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 VMS-ROM 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 data-driven VMS-ROM in the numerical simulation of the 1D Burgers equation and the 2D flow past a circular cylinder. The numerical results show that the data-driven VMS-ROM is significantly more accurate than standard ROMs.

Abstract:

We propose a new data-driven 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 VMS-ROM 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 VMS-ROM 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 VMS-ROM 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 data-driven VMS-ROM in the numerical simulation of the 1D Burgers equation and the 2D flow past a circular cylinder. The numerical results show that the data-driven VMS-ROM is significantly more accurate than standard ROMs.

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


(password: 1P&@+!5v)
video
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 long-standing 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 data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity 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 long-standing 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 data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations.

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


(password: 7v.#=9%N)
video
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 reduced-order-modeling 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 high-dimensional target functions, involving the Mori-Zwanzig 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 reduced-order-modeling 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 high-dimensional target functions, involving the Mori-Zwanzig 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 03, 2020
no colloquium
Fri, Jul 03,
2020
no colloquium
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 real-life 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 real-life 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,
June 19, 2020
Thomas M. Surowiec Philipps-Universität Marburg Optimization of Elliptic PDEs with Uncertain Inputs: Basic Theory and Numerical Stability ... more ... less
Fri, Jun 19,
2020
Thomas M. Surowiec,
Philipps-Universität 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 decision-making 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 PDE-constrained optimization. In particular, we are interested in finding answers to such questions as: How do the solutions behave in the large-data limit? Can we derive statements on the rate of convergence as the sample-size increases and mesh-size 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 risk-neutral PDE-constrained 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 decision-making 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 PDE-constrained optimization. In particular, we are interested in finding answers to such questions as: How do the solutions behave in the large-data limit? Can we derive statements on the rate of convergence as the sample-size increases and mesh-size 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 risk-neutral PDE-constrained problems.

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 vice-chair 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 vice-chair of the SIAM Dynamical Systems Group.

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,
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 steady-state 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 time-stepping methods quickly reach their limitations due to the enormous demand for storage [5]. Yet, more challenging than the afore-mentioned 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 so-called 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 block-diagonal preconditioners for solving the resulting stochastic Galerkin systems. The developed solvers are quite efficient in the reduction of temporal and storage requirements of the high-dimensional linear systems [1, 2]. Finally, we illustrate the effectiveness of our solvers with numerical experiments.

Keywords: Stochastic Galerkin system, iterative methods, PDE-constrained optimization, saddle-point system, low-rank solution, preconditioning, Schur complement.

References:

  1. Benner, S. Dolgov, A. Onwunta and M. Stoll, Low-rank solvers for unsteady Stokes-Brinkman optimal control prob- lem with random data, Computer Methods in Applied Mechanics and Engineering, 304, pp. 26 – 54, 2016.
  2. Benner, A. Onwunta and M. Stoll, Block-diagonal preconditioning for optimal control problems constrained by PDEs with uncertain inputs, SIAM Journal on Matrix Analysis and Applications, 37 (2), pp. 491 – 518, 2016.
  3. Bespalov and C. E. Powell and D. Silvester, Energy norm a posteriori error estimation for parametric operator equa- tions. SIAM Journal on Scientific Computing, 36 (2), pp. A339 – A363, 2013.
  4. Rosseel and G. N. Wells, Optimal control with stochastic PDE constraints and uncertain controls, Computer Methods in Applied Mechanics and Engineering, 213-216, pp. 152 – 167, 2012.
  5. Stoll and T. Breiten, A low-rank in time approach to PDE-constrained optimization, SIAM Journal on Scientific Computing, 37 (1), pp. B1 – B29, 2015.

About the speaker:

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 steady-state 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 time-stepping methods quickly reach their limitations due to the enormous demand for storage [5]. Yet, more challenging than the afore-mentioned 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 so-called 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 block-diagonal preconditioners for solving the resulting stochastic Galerkin systems. The developed solvers are quite efficient in the reduction of temporal and storage requirements of the high-dimensional linear systems [1, 2]. Finally, we illustrate the effectiveness of our solvers with numerical experiments.

Keywords: Stochastic Galerkin system, iterative methods, PDE-constrained optimization, saddle-point system, low-rank solution, preconditioning, Schur complement.

References:

  1. Benner, S. Dolgov, A. Onwunta and M. Stoll, Low-rank solvers for unsteady Stokes-Brinkman optimal control prob- lem with random data, Computer Methods in Applied Mechanics and Engineering, 304, pp. 26 – 54, 2016.
  2. Benner, A. Onwunta and M. Stoll, Block-diagonal preconditioning for optimal control problems constrained by PDEs with uncertain inputs, SIAM Journal on Matrix Analysis and Applications, 37 (2), pp. 491 – 518, 2016.
  3. Bespalov and C. E. Powell and D. Silvester, Energy norm a posteriori error estimation for parametric operator equa- tions. SIAM Journal on Scientific Computing, 36 (2), pp. A339 – A363, 2013.
  4. Rosseel and G. N. Wells, Optimal control with stochastic PDE constraints and uncertain controls, Computer Methods in Applied Mechanics and Engineering, 213-216, pp. 152 – 167, 2012.
  5. Stoll and T. Breiten, A low-rank in time approach to PDE-constrained optimization, SIAM Journal on Scientific Computing, 37 (1), pp. B1 – B29, 2015.

About the speaker:

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,
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.

Research Interaction and Training Seminars (RITS)

Conferences & Workshops