Self-adaptive and time divide-and-conquer physics-informed neural networks for two-phase flow simulations using interface tracking methods

Physics-informed neural networks (PINNs) are emerging as a promising artificial intelligence approach for solving complex two-phase flow simulations. A critical challenge in these simulations is an accurate representation of the gas–liquid interface using interface tracking methods. While numerous s...

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Veröffentlicht in:	Physics of fluids (1994) 2024-07, Vol.36 (7)
Hauptverfasser:	Zhou, Wen, Miwa, Shuichiro, Okamoto, Koji
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial intelligence Computational fluid dynamics Flow simulation Neural networks Partial differential equations Simulation Tracking Two phase flow
Online-Zugang:	Volltext
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