Towards Long-Term predictions of Turbulence using Neural Operators
Fernando Gonzalez François-Xavier Demoulin Simon Bernard
This paper explores Neural Operators to predict turbulent flows, focusing on
the Fourier Neural Operator (FNO) model. It aims to develop
reduced-order/surrogate models for turbulent flow simulations using Machine
Learning. Different model configurations are analyzed, with U-NET structures
(UNO and U-FNET) performing better than the standard FNO in accuracy and
stability. U-FNET excels in predicting turbulence at higher Reynolds numbers.
Regularization terms, like gradient and stability losses, are essential for
stable and accurate predictions. The study emphasizes the need for improved
metrics for deep learning models in fluid flow prediction. Further research
should focus on models handling complex flows and practical benchmarking
metrics.