701 citations · 776 across the 8 of their papers we have counts for
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physics.comp-ph2022★ 701 cited
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Chenxi Wu, Min Zhu, Qinyang Tan +2
Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs…
cs.LG2022★ 71 cited
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Xin-Yang Liu, Min Zhu, Lu Lu +2
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed…