Year: 2025
Type: conference-paper
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: Dynamic Mode Decomposition (DMD) and the Linearly-Recurrent Autoencoder Network (LRAN).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.
Institution: Bielefeld University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409480452