Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

arXiv:2402.11722v1 Announce Type: new
Abstract: Fourier Neural Operator (FNO) is a popular operator learning method, which has demonstrated state-of-the-art performance across many tasks. However, FNO is mainly used in forward prediction, yet a large family of applications rely on solving inverse problems. In this paper, we propose an invertible Fourier Neural Operator (iFNO) that tackles both the forward and inverse problems. We designed a series of invertible Fourier blocks in the latent channel space to share the model parameters, efficiently …

abstract applications art arxiv cs.lg family fourier operators paper performance popular prediction state tasks type