Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems. (arXiv:2205.06502v1 [cs.LG])

Reinforcement learning (RL) is highly suitable for devising control
strategies in the context of dynamical systems. A prominent instance of such a
dynamical system is the system of equations governing fluid dynamics. Recent
research results indicate that RL-augmented computational fluid dynamics (CFD)
solvers can exceed the current state of the art, for example in the field of
turbulence modeling. However, while in supervised learning, the training data
can be generated a priori in an offline manner, RL requires constant run-time …

arxiv computational dynamics fluid dynamics hpc learning reinforcement reinforcement learning systems