Jul 11, 2022 · Abstract:Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural ...
Deep learning surrogate models have shown promise in solving partial differential equations. (PDEs). Among them, the Fourier neural operator (FNO) achieves ...
Mar 6, 2024 · In this work, we propose a new framework, viz., Geo-FNO, to solve PDEs on arbitrary geometries. Geo-FNO learns to deform the input (physical) ...
May 2, 2024 · This limits the design space to be similar to the training data, and may not generalize to new geometries and scenarios. In contrast, we develop ...
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Fourier neural operator with learned deformations for pdes on general geometries. Z Li, DZ Huang, B Liu, A Anandkumar. Journal of Machine Learning Research 24 ( ...
In this work, we propose a new framework, viz., geo-FNO, to solve PDEs on arbitrary geometries. Geo-FNO learns to deform the input (physical) domain, which may ...
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries · Zong-Yi Li, D. Huang, +1 author. Anima Anandkumar · Published in arXiv.org 11 ...
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows.
May 2, 2024 · In this work, we propose a new framework, viz., geo-FNO, tosolve PDEs on arbitrary geometries. Geo-FNO learns to deform the input(physical) ...
Aug 26, 2022 · In this work, we propose a new framework, viz., geo-FNO, to solve PDEs on arbitrary geometries. Geo-FNO learns to deform the input (physical) ...
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