Non-equispaced Fourier Neural Solvers for PDEs

• URL: http://arxiv.org/abs/2212.04689v2
• Date: Sat, 18 Mar 2023 08:52:33 GMT
• Title: Non-equispaced Fourier Neural Solvers for PDEs
• Authors: Haitao Lin, Lirong Wu, Yongjie Xu, Yufei Huang, Siyuan Li, Guojiang Zhao, Stan Z. Li
• Abstract summary: textscNFS is the first ML-based method with mesh invariant inference ability to successfully model turbulent flows in non-equispaced scenarios. It achieves superior performance with $42.85%$ improvements on MAE, and is able to handle non-equispaced data with a tiny loss of accuracy.
• Score: 43.42089594581029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving partial differential equations is difficult. Recently proposed neural resolution-invariant models, despite their effectiveness and efficiency, usually require equispaced spatial points of data. However, sampling in spatial domain is sometimes inevitably non-equispaced in real-world systems, limiting their applicability. In this paper, we propose a Non-equispaced Fourier PDE Solver (\textsc{NFS}) with adaptive interpolation on resampled equispaced points and a variant of Fourier Neural Operators as its components. Experimental results on complex PDEs demonstrate its advantages in accuracy and efficiency. Compared with the spatially-equispaced benchmark methods, it achieves superior performance with $42.85\%$ improvements on MAE, and is able to handle non-equispaced data with a tiny loss of accuracy. Besides, to our best knowledge, \textsc{NFS} is the first ML-based method with mesh invariant inference ability to successfully model turbulent flows in non-equispaced scenarios, with a minor deviation of the error on unseen spatial points.

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