Fourier Neural Operators for Rayleigh-Bénard Convection

July 02, 2026 · Grace Period · 🏛 ICCS 2026, Lecture Notes in Computer Science, vol 16784. Springer, Cham

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Authors Chelsea Maria John, Thibaut Lunet, Sebastian Götschel, Andreas Herten, Stefan Kesselheim, Daniel Ruprecht arXiv ID 2607.02088 Category cs.LG: Machine Learning Cross-listed physics.flu-dyn Citations 0 Venue ICCS 2026, Lecture Notes in Computer Science, vol 16784. Springer, Cham
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.
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