Web: http://arxiv.org/abs/2209.03151

Sept. 19, 2022, 1:12 a.m. | Shihong Zhang, Chi Zhang, Bosen Wang

cs.LG updates on arXiv.org arxiv.org

Compared with conventional numerical approaches to solving partial
differential equations (PDEs), physics-informed neural networks (PINN) have
manifested the capability to save development effort and computational cost,
especially in scenarios of reconstructing the physics field and solving the
inverse problem. Considering the advantages of parameter sharing, spatial
feature extraction and low inference cost, convolutional neural networks (CNN)
are increasingly used in PINN. However, some challenges still remain as
follows. To adapt convolutional PINN to solve different PDEs, considerable
effort is usually …

arxiv network neural network physics

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