Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Understand

Fourier Neural Operator (FNO) is a powerful and popular operator learning method.

• However, FNO is mainly used in forward prediction, yet a great many applications rely on solving inverse problems.
• In this paper, we propose an invertible Fourier Neural Operator (iFNO) for jointly tackling the forward and inverse problems.
• We developed a series of invertible Fourier blocks in the latent channel space to share the model parameters, exchange the information, and mutually regularize the learning for the bi-directional tasks.