Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each mate...

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Veröffentlicht in:	International journal of computational methods in engineering science and mechanics Jg. 26; H. 2; S. 187 - 207
Hauptverfasser:	Roy, Sumanta, Annavarapu, Chandrasekhar, Roy, Pratanu, Valiveti, Dakshina M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 04.03.2025
Schlagworte:	coupled problems deep neural network ENGINEERING physics-constrained machine learning Physics-informed neural networks poromechanics scientific machine learning
ISSN:	1550-2287, 1550-2295
Online-Zugang:	Volltext
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