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Organizing Committee

Peter Benner
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg

Albert Cohen
Sorbonne Université

Olga Mula
Paris Dauphine University

Akil Narayan
University of Utah

Karen Veroy-Grepl
TU Eindhoven

Abstract

Mathematical models of scientific applications often involve simulations with a large number of degrees of freedom that strain even the most efficient of algorithms. A clear need is the rigorous development of models with reduced complexity that retain fidelity to the application. Mathematics-based reduced-order modeling applies techniques in nonlinear approximation, projection-based discretizations, sparse surrogate construction, and high-dimensional approximation, in order to construct a model surrogate with near-optimal approximation properties. This workshop focuses on theoretical and algorithmic advances in mathematics-based model order reduction of various types: reduced basis methods, projection-based methods for dynamical systems, and sparse and low-rank approximations in high dimensions.

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Lecture Videos

Further exploitation of the RB framework

Yvon Maday

Sorbonne Université

February 17, 2020 • 12:00 AM

Mathematics of Reduced Order Models

Feb 17 - 21, 2020

Organizing Committee

Abstract

Lecture Videos

Further exploitation of the RB framework

Sampling low-dimensional Markovian dynamics for learning certified reduced ...

Reduced Models in State Estimation

Linear and nonlinear methods for model reduction

The Loewner framework for model reduction of large-scale systems

Embedding properties of data-driven dissipative reduced order models

Operator inference for non-intrusive model reduction of non-polynomial nonl...

The Loewner Framework for Model Reduction of Flow Equations

Adaptivity concepts for POD reduced-order modeling

A reduced basis method for parametrized variational inequalities applied to...

Closed-loop controls for fluids

Predictive data science for physical systems- From model reduction to scien...

Data-driven Modeling and Optimization of Dissipative Dynamics

Model reduction for port-Hamiltonian differential-algebraic systems

Automatic Generation of Minimal and Reduced Models for Structured Parametri...

A Rank-Minimization Approach to Learning Dynamical Systems from Frequency R...

Convolutional autoencoders and LSTMs - using deep learning to overcome Kolm...

H2-Optimal Model Reduction Using Projected Nonlinear Least Squares

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

Mathematics of Reduced Order Models

Feb 17 - 21, 2020

Organizing Committee

Abstract

Confirmed Speakers & Participants

Lecture Videos

Further exploitation of the RB framework

Sampling low-dimensional Markovian dynamics for learning certified reduced ...

Reduced Models in State Estimation

Linear and nonlinear methods for model reduction

The Loewner framework for model reduction of large-scale systems

Embedding properties of data-driven dissipative reduced order models

Operator inference for non-intrusive model reduction of non-polynomial nonl...

The Loewner Framework for Model Reduction of Flow Equations

Adaptivity concepts for POD reduced-order modeling

A reduced basis method for parametrized variational inequalities applied to...

Closed-loop controls for fluids

Predictive data science for physical systems- From model reduction to scien...

Data-driven Modeling and Optimization of Dissipative Dynamics

Model reduction for port-Hamiltonian differential-algebraic systems

Automatic Generation of Minimal and Reduced Models for Structured Parametri...

A Rank-Minimization Approach to Learning Dynamical Systems from Frequency R...

Convolutional autoencoders and LSTMs - using deep learning to overcome Kolm...

H2-Optimal Model Reduction Using Projected Nonlinear Least Squares

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

Confirmed Speakers & Participants