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Scientific multi-agent reinforcement learning for wall-models of turbulent flows

Paper title Scientific multi-agent reinforcement learning for wall-models of turbulent flows

Bae and Koumoutsakos (Harvard, Caltech) · Aero & CFD · Public: code, data

Paper arXiv 2106.11144PDF

Learns the wall model of a wall-modelled LES by multi-agent reinforcement learning. Agents along the wall read the velocity and its wall-normal gradient at one off-wall point and scale the local wall-shear stress by a factor between 0.9 and 1.1 at each step; the reward is the improvement in the predicted wall-shear stress. One variant (LLWM) takes instantaneous log-law coefficients as its state.

Published
Nature Communications

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