# Fourier Neural Operator for Parametric Partial Differential Equations

@article{Li2021FourierNO, title={Fourier Neural Operator for Parametric Partial Differential Equations}, author={Zong-Yi Li and Nikola B. Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar}, journal={ArXiv}, year={2021}, volume={abs/2010.08895} }

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation…

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