Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators

Michiel Straat, Thorben Markmann, Barbara Hammer
1/27/2025
physics.flu-dyncs.LG

No official code found. Best implementation: HammerLabML/RBC-FNO-Surrogate (0 stars, MIT, last commit 2026-03-25).

Abstract

We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics: the Dynamic Mode Decomposition and the Linearly-Recurrent Autoencoder Network. We regard Direct Numerical Simulations (DNS) of the RBC equations as the ground truth on which the models are trained and evaluated in different settings. The FNO performs favorably when compared to the DMD and LRAN and its predictions are fast and highly accurate for this task. Additionally, we show its zero-shot super-resolution ability for the convection dynamics. The FNO model has a high potential to be used in downstream tasks such as flow control in RBC.

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Code Implementations(1)

HammerLabML/RBC-FNO-SurrogateCommunity100%
MIT
00Shell, PythonNov 22, 20247 months agoMIT

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Cite this paper

@article{straat2025solving,
  title  = {Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators},
  author = {Michiel Straat and Thorben Markmann and Barbara Hammer},
  year   = {2025},
  eprint = {2501.16209},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.16209v1}
}

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