A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

•A novel physics-informed neural network reduced order model is introduced.•It is based on nonlinear manifold solution representation.•A sparse shallow neural network structure is used for the nonlinear manifold.•The existing numerical discretization of the full order model is utilized.•Advection-do...

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Veröffentlicht in:	Journal of computational physics Jg. 451; S. 110841
Hauptverfasser:	Kim, Youngkyu, Choi, Youngsoo, Widemann, David, Zohdi, Tarek
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 15.02.2022 Elsevier Science Ltd
Schlagworte:	Advection Air flow Atmospheric models Burgers equation Computational physics Hyper-reduction Mathematical models Neural networks Nonlinear dynamical system Nonlinear manifold solution representation Numerical methods Operators (mathematics) Physics-informed neural network Reduced order model Reduced order models Reduction Solution space Subspaces Traffic flow Hyper-reduction Physics-informed neural network Nonlinear dynamical system Nonlinear manifold solution representation Reduced order model
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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