PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

•Enable CNN-based physics-informed deep learning for PDEs on irregular domain.•The proposed network can be trained without any labeled data.•Boundary conditions are strictly encoded in a hard manner.•Investigated complex parametric PDEs, e.g., Naiver-Stokes with varying geometries.•Shows improvement...

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Veröffentlicht in:	Journal of computational physics Jg. 428; S. 110079
Hauptverfasser:	Gao, Han, Sun, Luning, Wang, Jian-Xun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 01.03.2021 Elsevier Science Ltd
Schlagworte:	Artificial neural networks Boundary conditions Computational fluid dynamics Computational physics Coordinate transformations Domains Label-free Machine learning Navier-Stokes Neural networks Parameterization Partial differential equations Physics Physics-constrained deep learning Physics-informed neural networks Poisson equation Steady state Surrogate modeling Thermodynamics Physics-constrained deep learning Navier-Stokes Partial differential equations Label-free Surrogate modeling Physics-informed neural networks
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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