Respecting causality for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 421; H. C; S. 116813
Hauptverfasser:	Wang, Sifan, Sankaran, Shyam, Perdikaris, Paris
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.03.2024 Elsevier
Schlagworte:	Chaotic systems Computational physics Deep learning Partial differential equations Deep learning Computational physics Partial differential equations Chaotic systems
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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