Automating turbulence modelling by multi-agent reinforcement learning

Abstract:

Turbulent flow models are critical for applications such as aircraft design, weather forecasting and climate prediction. Existing
models are largely based on physical insight and engineering intuition. More recently, machine learning has been contributing
to this endeavour with promising results. However, all efforts have focused on supervised learning, which is difficult to generalize beyond training data. Here we introduce multi-agent reinforcement learning as an automated discovery tool of turbulence
models. We demonstrate the potential of this approach on large-eddy simulations of isotropic turbulence, using the recovery of
statistical properties of direct numerical simulations as a reward. The closure model is a control policy enacted by cooperating
agents, which detect critical spatio-temporal patterns in the flow field to estimate the unresolved subgrid-scale physics. Results
obtained with multi-agent reinforcement learning algorithms based on experience replay compare favourably with established
modelling approaches. Moreover, we show that the learned turbulence models generalize across grid sizes and flow conditions.