most citedRevisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

13 citations · 13 across the 1 of their papers we have counts for

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cs.LG2022★ 13 cited

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Wensheng Li, Chao Zhang, Chuncheng Wang +2

Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of…