Fourier Neural Operators for Rayleigh-Bénard Convection
Chelsea Maria John, Thibaut Lunet, Sebastian Götschel, Andreas Herten, Stefan Kesselheim, Daniel Ruprecht
cs.LG physics.flu-dyn
Abstract
We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving higher accuracy than a standard FNO baseline. The resulting model is compact (314k parameters, 1.26 MB) and fast (7 ms inference), while maintaining similar accuracy as demonstrated in previous benchmarks. We show that although FNOs generalize to finer meshes, accuracy remains limited by the resolution of the training data.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.