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

DOI10.1051/m2an/2022013zbMath1492.37073arXiv2007.13153OpenAlexW4210995191WikidataQ114105414 ScholiaQ114105414MaRDI QIDQ5061538

Cecilia Pagliantini, Nicolò Ripamonti, Jan S. Hesthaven

Publication date: 11 March 2022

Published in: ESAIM: Mathematical Modelling and Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.13153

zbMATH Keywords

symplectic manifoldsadaptive algorithmsHamiltonian dynamicsreduced basis methodsdynamical low-rank approximation

Mathematics Subject Classification ID

Numerical methods for Hamiltonian systems including symplectic integrators (65P10) Discretization methods and integrators (symplectic, variational, geometric, etc.) for dynamical systems (37M15) Dynamical systems in numerical analysis (37N30) Model reduction in optics and electromagnetic theory (78M34)

Related Items

Reduced basis methods for time-dependent problems, An adaptive dynamical low rank method for the nonlinear Boltzmann equation, Global energy preserving model reduction for multi-symplectic PDEs, Gradient-Preserving Hyper-Reduction of Nonlinear Dynamical Systems via Discrete Empirical Interpolation, Neural Galerkin schemes with active learning for high-dimensional evolution equations, Stable Rank-Adaptive Dynamically Orthogonal Runge–Kutta Schemes, Optimization on the symplectic Stiefel manifold: SR decomposition-based retraction and applications, Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds, Adaptive symplectic model order reduction of parametric particle-based Vlasov–Poisson equation, Rank-adaptive dynamical low-rank integrators for first-order and second-order matrix differential equations

Uses Software

• symrcm
• redbKIT

Cites Work

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