Fourier Neural Operators for Rayleigh-B'enard Convection
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Abstract (arXiv)
We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-B'enard 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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References · 5
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 473
- 2025TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansioncited by 8
- 2025Solving Turbulent Rayleigh-B'enard Convection using Fourier Neural Operatorscited by 5
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