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

Prof. Jane Bae from Caltech speaking in the UW Data-driven methods in science and engineering seminar on Nov. 12, 2021.

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Abstract: The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to higher Reynolds numbers in reproducing key flow quantities.

Видео Jane Bae - Wall-models of turbulent flows via scientific multi-agent reinforcement learning канала Physics Informed Machine Learning