Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
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Publication:2127404
DOI10.1016/j.physd.2020.132614zbMath1489.93013arXiv1911.03808OpenAlexW3035079212MaRDI QIDQ2127404
Han Gao, Jian-Xun Wang, Matthew J. Zahr
Publication date: 20 April 2022
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.03808
Artificial neural networks and deep learning (68T07) Nonlinear systems in control theory (93C10) System structure simplification (93B11) Large-scale systems (93A15)
Related Items (7)
A pruning algorithm preserving modeling capabilities for polycrystalline data ⋮ Transformers for modeling physical systems ⋮ Operator inference for non-intrusive model reduction with quadratic manifolds ⋮ CD-ROM: complemented deep -- reduced order model ⋮ Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds ⋮ Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation ⋮ Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs
Uses Software
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