Web: http://arxiv.org/abs/2205.01051

May 6, 2022, 1:12 a.m. | Wei Peng, Weien Zhou, Xiaoya Zhang, Wen Yao, Zheliang Liu

cs.LG updates on arXiv.org arxiv.org

Learning solutions of partial differential equations (PDEs) with
Physics-Informed Neural Networks (PINNs) is an attractive alternative approach
to traditional solvers due to its flexibility and ease of incorporating
observed data. Despite the success of PINNs in accurately solving a wide
variety of PDEs, the method still requires improvements in terms of
computational efficiency. One possible improvement idea is to optimize the
generation of training point sets. Residual-based adaptive sampling and
quasi-uniform sampling approaches have been each applied to improve the …

arxiv networks neural neural networks physics

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