A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scatt...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 403; H. PA; S. 115671
Hauptverfasser:	Wu, Chenxi, Zhu, Min, Tan, Qinyang, Kartha, Yadhu, Lu, Lu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.01.2023 Elsevier BV Elsevier
Schlagworte:	Adaptive sampling Hammersley sequence Hypercubes Inverse problems Latin hypercube sampling Neural networks Non-adaptive uniform sampling Partial differential equations Physics-informed neural networks Random sampling Resampling Residual point distribution Residual-based adaptive sampling Sampling methods Uniform sampling with resampling Residual point distribution Non-adaptive uniform sampling Partial differential equations Residual-based adaptive sampling Uniform sampling with resampling Physics-informed neural networks
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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