Gradient-enhanced PINN with residual unit for studying forward-inverse problems of variable coefficient equations

Physics-informed neural network (PINN) is a powerful emerging method for studying forward-inverse problems of partial differential equations (PDEs), even from limited sample data. Variable coefficient PDEs, which model real-world phenomena, are of considerable physical significance and research valu...

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Veröffentlicht in:	Physica. D Jg. 481; S. 134764
Hauptverfasser:	Zhou, Hui-Juan, Chen, Yong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2025
Schlagworte:	Data-driven solution Function discovery Physics-informed neural network Residual network Variable coefficient equation Physics-informed neural network Function discovery Variable coefficient equation Residual network Data-driven solution
ISSN:	0167-2789
Online-Zugang:	Volltext
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