Self-adaptive physics-informed neural networks

Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accur...

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Veröffentlicht in:	Journal of computational physics Jg. 474; S. 111722
Hauptverfasser:	McClenny, Levi D., Braga-Neto, Ulisses M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.02.2023
Schlagworte:	Numerical methods for PDE Physics-informed neural networks Scientific machine learning Numerical methods for PDE Physics-informed neural networks Scientific machine learning
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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