RL turbulence closure
Automating turbulence modelling by multi-agent reinforcement learning
Paper title Automating turbulence modelling by multi-agent reinforcement learning
Paper arXiv 2005.09023PDF
Treats the subgrid-scale closure of a large-eddy simulation as a control policy. Agents spread over the grid observe invariants of the local velocity gradient and Hessian together with the global energy spectrum, and set the local Smagorinsky coefficient. One shared network is trained by reinforcement learning with the LES running in the loop, rewarded for matching the energy spectrum of DNS.
- Published
- Nature Machine Intelligence
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