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Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradients
Feb. 28, 2024, 5:43 a.m. | Rahul Sundar, Didier Lucor, Sunetra Sarkar
cs.LG updates on arXiv.org arxiv.org
Abstract: Recently immersed boundary method-inspired physics-informed neural networks (PINNs) including the moving boundary-enabled PINNs (MB-PINNs) have shown the ability to accurately reconstruct velocity and recover pressure as a hidden variable for unsteady flow past moving bodies. Considering flow past a plunging foil, MB-PINNs were trained with global physics loss relaxation and also in conjunction with a physics-based undersampling method, obtaining good accuracy. The purpose of this study was to investigate which input spatial subdomain contributes to …
abstract arxiv cs.lg flow function hidden loss moving networks neural networks physics physics.flu-dyn physics-informed through training type understanding
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