Fourier Neural Operators for Rayleigh-Bénard Convection
Incremental improvement for domain-specific fluid dynamics modeling.
The authors improved Fourier Neural Operators for 2D Rayleigh-Bénard convection by predicting time increments, achieving higher accuracy than a standard FNO baseline with a compact model (314k parameters, 1.26 MB) and fast inference (7 ms).
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.