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most citedReference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations

1 citations · 2 across the 5 of their papers we have counts for

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cs.LG2024★ 1 cited

Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations

Ze Cheng, Zhongkai Hao, Xiaoqiang Wang +6

For partial differential equations on domains of arbitrary shapes, existing works of neural operators attempt to learn a mapping from geometries to solutions. It often requires a l…

cs.LG2024★ 1 cited

Preconditioning for Physics-Informed Neural Networks

Songming Liu, Chang Su, Jiachen Yao +4

Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the con…