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Organizing Committee

Kevin Carlberg
University of Washington

Yanlai Chen
University of Massachusetts, Dartmouth

Francisco Chinesta
ENSAM

Misha Kilmer
Tufts University

Yvon Maday
Sorbonne Université

Gianluigi Rozza
SISSA – International School for Advanced Studies

Abstract

Mathematical advances that reduce the complexity of models are complemented by algorithms that achieve the desired reduction in computational effort. This workshop focuses on the synthesis and development of algorithmic approaches to model order reduction. These methods tackle fundamental problems in structure- and topology-preserving reductions, low-rank models and dimension reduction, multi-level approaches, and empirical interpolation and approximations, etc. Complementary approaches that target computational efficiency include strategies with offline and online phases and divide-and-conquer algorithms.

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

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Workshop Schedule

Monday, March 23, 2020

9:50 - 10:00 AM EDT

Welcome - ICERM Director

10:00 - 10:45 AM EDT

Reduced order modelling in electronic structure calculations - first attempts - Benjamin Stamm, RWTH Aachen University

11:00 - 11:15 AM EDT

Coffee/Tea Break

11:15 AM - 12:00 PM EDT

Tensor star-M product SVDs for compression, with applications in POD and in system identification. - Misha Kilmer, Tufts University

12:15 - 1:30 PM EDT

Break for Lunch / Free Time

1:30 - 2:15 PM EDT

Novel tensor framework for model reduction and neural networks - Shashanka Ubaru, IBM Research

2:30 - 2:45 PM EDT

Coffee/Tea Break

2:45 - 3:30 PM EDT

Reduced basis methods for linear fractional elliptic problems - Akil Narayan, University of Utah

3:30 - 3:45 PM EDT

Close the day

Tuesday, March 24, 2020

10:00 - 10:45 AM EDT

Localized model order reduction - Kathrin Smetana, University of Twente

11:00 - 11:15 AM EDT

Coffee/Tea Break

11:15 AM - 12:00 PM EDT

A Time-Multiscale Model Order Reduction Method In Nonlinear Solid Mechanics - Pierre Ladeveze, Ens Paris-Saclay

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Poster Session Gallery

Learning block-oriented nonlinear models of dynamical systems from data

Ion Victor Gosea

Gradient-Based Recovery of Linear Projections in Multi-Index Models

Michael Patrick Martin

System-Theoretic Model Reduction in pyMOR

Petar Mlinarić

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Elizabeth Qian

Data-Driven Discovery of Emergent Behaviors

Ming Zhong

Lecture Videos

Novel tensor framework for model reduction and neural networks

Shashanka Ubaru

IBM T.J. Watson Research Center

March 23, 2020 • 12:00 AM

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

Mar 23 - 27, 2020

Organizing Committee

Abstract

Workshop Schedule

Monday, March 23, 2020

Tuesday, March 24, 2020

Poster Session Gallery

Learning block-oriented nonlinear models of dynamical systems from data

Gradient-Based Recovery of Linear Projections in Multi-Index Models

System-Theoretic Model Reduction in pyMOR

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Data-Driven Discovery of Emergent Behaviors

Lecture Videos

Novel tensor framework for model reduction and neural networks

Reduced basis methods for linear fractional elliptic problems

Localized model order reduction

A Time-Multiscale Model Order Reduction Method In Nonlinear Solid Mechanics...

Model Order Reduction of Energy Networks with a Focus on Hyperbolic Systems...

Goal-oriented adaptivity within the Generalized Multiscale Finite Element M...

The p-AAA algorithm for data driven modeling of parametric dynamical system...

Data-Driven Variational Multiscale Reduced Order Models

POD Reduced-Order Modeling of Complex Fluid Flows

A Reduced Order Modeling approach for PDEs with bifurcating solutions

Sensor Selection for Bayesian Inverse Problems and Data Assimilation via Di...

Efficient randomized algorithms for subspace system identification

Structured Autoencoders for Operator-theoretic decomposition and Model redu...

Randomization for the Efficient Reduced Order Models in Diffuse Optical Tom...

Reliable and efficient model reduction of parametrized aerodynamics problem...

Interpretable data-driven model reduction for multiscale nonlinear dynamics...

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

Mathematics of Reduced Order Models

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

Mar 23 - 27, 2020

Organizing Committee

Abstract

Confirmed Speakers & Participants

Workshop Schedule

Monday, March 23, 2020

Tuesday, March 24, 2020

Poster Session Gallery

Learning block-oriented nonlinear models of dynamical systems from data

Gradient-Based Recovery of Linear Projections in Multi-Index Models

System-Theoretic Model Reduction in pyMOR

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Data-Driven Discovery of Emergent Behaviors

Lecture Videos

Novel tensor framework for model reduction and neural networks

Reduced basis methods for linear fractional elliptic problems

Localized model order reduction

A Time-Multiscale Model Order Reduction Method In Nonlinear Solid Mechanics...

Model Order Reduction of Energy Networks with a Focus on Hyperbolic Systems...

Goal-oriented adaptivity within the Generalized Multiscale Finite Element M...

The p-AAA algorithm for data driven modeling of parametric dynamical system...

Data-Driven Variational Multiscale Reduced Order Models

POD Reduced-Order Modeling of Complex Fluid Flows

A Reduced Order Modeling approach for PDEs with bifurcating solutions

Sensor Selection for Bayesian Inverse Problems and Data Assimilation via Di...

Efficient randomized algorithms for subspace system identification

Structured Autoencoders for Operator-theoretic decomposition and Model redu...

Randomization for the Efficient Reduced Order Models in Diffuse Optical Tom...

Reliable and efficient model reduction of parametrized aerodynamics problem...

Interpretable data-driven model reduction for multiscale nonlinear dynamics...

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

Mathematics of Reduced Order Models

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

Confirmed Speakers & Participants