A model hierarchy for predicting the flow in stirred tanks with physics-informed neural networks

This paper explores the potential of Physics-Informed Neural Networks (PINNs) to serve as Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors (STRs). We solve the two-dimensional stationary Navier-Stokes equations within a geometrically intricate domain and explore...

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Veröffentlicht in:	Advances in Computational Science and Engineering Jg. 2; H. 2; S. 91 - 129
Hauptverfasser:	Trávníková, Veronika, Wolff, Daniel, Dirkes, Nico, Elgeti, Stefanie, von Lieres, Eric, Behr, Marek
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	01.06.2024
Schlagworte:	stirred tank reactors domain decomposition Physics-informed neural networks reduced order modeling Navier-Stokes equations
ISSN:	2837-1739, 2837-1739
Online-Zugang:	Volltext
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