Neural networks for multi-fidelity ensemble large-eddy simulations

The computing expense of uncertainty quantification or optimization in computational fluid dynamics can be reduced by using two levels of solution fidelity: one that is high - such as a high-resolution large-eddy simulation (LES) - and one that is low - such as a coarse resolution LES. In this work, we explore a method that uses information from the higher fidelity level to inform the lower fidelity simulations using neural networks. This learned function is a source term in the momentum equations of the low fidelity simulations, and is trained to account for the discretization and filtering errors incurred. We explore different choices of features and training methodology, and evaluate the performance of the method in wall-bounded turbulence.