Publication

Rank-adaptive structure-preserving model order reduction of Hamiltonian systems

Related publications (38)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Fabio Nobile, Eleonora Musharbash

In this paper we propose a dynamical low-rank strategy for the approximation of second order wave equations with random parameters. The governing equation is rewritten in Hamiltonian form and the approximate solution is expanded over a set of

2S

dynamica ...

2017

Rank-adaptive structure-preserving model order reduction of Hamiltonian systems

Graph Chatbot

Chat with Graph Search

Structure-preserving approaches and data-driven closure modeling for model order reduction

Generalised Howe duality and injectivity of induction: the symplectic case

Structure-Preserving Reduced Basis Methods For Poisson Systems

Scattering Map for the Vlasov–Poisson System

Symplectic dynamical low rank approximation of wave equations with random parameters

Dynamical Reduced Basis Methods for Hamiltonian Systems

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a Nonlinear Poisson Structure

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

Symplectic Model-Reduction with a Weighted Inner Product

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Symplectic dynamical low rank approximation of wave equations with random parameters

Dynamical Reduced Basis Methods for Hamiltonian Systems

Symplectic Model-Reduction with a Weighted Inner Product

Scattering Map for the Vlasov–Poisson System

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Structure-Preserving Reduced Basis Methods For Poisson Systems

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a Nonlinear Poisson Structure

Generalised Howe duality and injectivity of induction: the symplectic case

Structure-preserving approaches and data-driven closure modeling for model order reduction