Fourier Neural Operators for Rayleigh-Bénard Convection
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.
| Authors | Chelsea Maria John, Thibaut Lunet, Sebastian Götschel et al. |
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| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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