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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Vydáno v:	Journal of computational physics Ročník 451; s. 110841
Hlavní autoři:	Kim, Youngkyu, Choi, Youngsoo, Widemann, David, Zohdi, Tarek
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cambridge Elsevier Inc 15.02.2022 Elsevier Science Ltd
Témata:	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
On-line přístup:	Získat plný text
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