Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials

Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse engineering domains thanks to their capacity to integrate physical laws into deep learning models. Recently, geometry-aware PINN-based approaches that employ the strong form of underlying physical system equations...

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Veröffentlicht in:	arXiv.org 2024-05
Hauptverfasser:	Thi Nguyen Khoa Nguyen, Thibault Dairay, Meunier, Raphaël, Millet, Christophe, Mougeot, Mathilde
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational efficiency Deep learning Energy methods Geometry Mechanics Neural networks Physics Potential energy Solid mechanics
Online-Zugang:	Volltext
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