Automating Turbulence Modeling by Multi-Agent Reinforcement Learning
- URL: http://arxiv.org/abs/2005.09023v2
- Date: Fri, 23 Oct 2020 11:25:26 GMT
- Title: Automating Turbulence Modeling by Multi-Agent Reinforcement Learning
- Authors: Guido Novati, Hugues Lascombes de Laroussilhe, and Petros Koumoutsakos
- Abstract summary: We introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models.
We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence.
- Score: 4.784658158364452
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The modeling of turbulent flows is critical to scientific and engineering
problems ranging from aircraft design to weather forecasting and climate
prediction. Over the last sixty years numerous turbulence models have been
proposed, largely based on physical insight and engineering intuition. Recent
advances in machine learning and data science have incited new efforts to
complement these approaches. To date, all such efforts have focused on
supervised learning which, despite demonstrated promise, encounters
difficulties in generalizing beyond the distributions of the training data. In
this work we introduce multi-agent reinforcement learning (MARL) as an
automated discovery tool of turbulence models. We demonstrate the potential of
this approach on Large Eddy Simulations of homogeneous and isotropic turbulence
using as reward the recovery of the statistical properties of Direct Numerical
Simulations. Here, the closure model is formulated as a control policy enacted
by cooperating agents, which detect critical spatio-temporal patterns in the
flow field to estimate the unresolved sub-grid scale (SGS) physics. The present
results are obtained with state-of-the-art algorithms based on experience
replay and compare favorably with established dynamic SGS modeling approaches.
Moreover, we show that the present turbulence models generalize across grid
sizes and flow conditions as expressed by the Reynolds numbers.
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