Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
This work addresses convection modeling for natural and industrial applications, but it is incremental as it applies an existing method to a new domain.
The paper tackled simulating turbulent Rayleigh-Bénard Convection by training Fourier Neural Operator surrogate models, achieving fast and highly accurate predictions compared to existing methods like Dynamic Mode Decomposition and Linearly-Recurrent Autoencoder Networks, with demonstrated zero-shot super-resolution ability.
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.