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

arXiv:2405.03427v1 Announce Type: new
Abstract: 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 have been developed with the aim of integrating geometric information into PINNs. Despite ongoing research, the assessment of PINNs in problems with various geometries remains an active area of investigation. In this work, we introduce a novel …

abstract application arxiv capacity cs.lg deep learning diverse domains energy engineering form framework geometry laws materials networks neural networks physics physics-informed pinn type