Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse...

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Vydané v:	Computer methods in applied mechanics and engineering Ročník 393; číslo C; s. 114823
Hlavní autori:	Yu, Jeremy, Lu, Lu, Meng, Xuhui, Karniadakis, George Em
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.04.2022 Elsevier BV Elsevier
Predmet:	Deep learning ENGINEERING Gradient-enhanced MATHEMATICS AND COMPUTING Neural networks Partial differential equations Physics Physics-informed neural networks Residual-based adaptive refinement Training Deep learning Partial differential equations Residual-based adaptive refinement Gradient-enhanced Physics-informed neural networks
ISSN:	0045-7825, 1879-2138
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