Wall-models of turbulent flows via scientific multi-agent reinforcement learning

We introduce a methodology for the semi-automated discovery of wall models for large-eddy simulations (LES). The methodology, scientific multi-agent reinforcement learning (SciMARL), fuses the numerical discertization of the flow governing equations with multi-agent reinforcement learning.  In SciMARL, the discretization points  act simultaneously  as cooperating agents that learn to supply the LES closure model. A particular advantage of  SciMARL over other machine learning methodologies is its generalisation capabilities with limited data. The agents self-learn closures as action policies that generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL opens new capabilities for the modeling and simulation of turbulent flows.