GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern heterogeneous hardware. However, its training is still t...

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Veröffentlicht in:	Finite elements in analysis and design Jg. 228; S. 104047
Hauptverfasser:	Chen, Yanlai, Koohy, Shawn
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2024
Schlagworte:	Meta-learning model order reduction Network of networks Non-intrusive learning Parametric PDEs Physics-Informed Neural Networks Physics-Informed Neural Networks Parametric PDEs Non-intrusive learning model order reduction Meta-learning Network of networks
ISSN:	0168-874X, 1872-6925
Online-Zugang:	Volltext
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