Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

Despite the great promise of the physics-informed neural networks (PINNs) in solving forward and inverse problems, several technical challenges are present as roadblocks for more complex and realistic applications. First, most existing PINNs are based on point-wise formulation with fully-connected n...

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Vydáno v:	Computer methods in applied mechanics and engineering Ročník 390; s. 114502
Hlavní autoři:	Gao, Han, Zahr, Matthew J., Wang, Jian-Xun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 15.02.2022 Elsevier BV
Témata:	Artificial neural networks Boundary conditions Computational geometry Continuity (mathematics) Convexity Graph convolutional neural networks Inverse problem Inverse problems Mechanics Neural networks Optimization Partial differential equations Physics-informed machine learning Polynomials Training Physics-informed machine learning Mechanics Graph convolutional neural networks Partial differential equations Inverse problem
ISSN:	0045-7825, 1879-2138
On-line přístup:	Získat plný text
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