Sergey Litvinov

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.