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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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 390; S. 114502
Hauptverfasser:	Gao, Han, Zahr, Matthew J., Wang, Jian-Xun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.02.2022 Elsevier BV
Schlagworte:	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
Online-Zugang:	Volltext
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