This work introduces a novel framework that learns physical sample residuals instead of direct mappings for PDE solving, significantly improving neural operator generalization in data-limited scenarios.

We propose GyroSwin, a 5D Swin Transformer to learn a 5D PDE commonly encountered in Gyrokinetics to simulate turbulence in a nuclear fusion reactor.