A review on Deep Reinforcement Learning for Fluid Mechanics

Abstract

Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previously out of reach due to a combination of non-linearity and high dimensionality. In the last few years, it has spread in the field of computational mechanics, and particularly in fluid dynamics, with recent applications in flow control and shape optimization. In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems. In addition, we present recent results that further illustrate the potential of DRL in Fluid Mechanics. The coupling methods used in each case are covered, detailing their advantages and limitations. Our review also focuses on the comparison with classical methods for optimal control and optimization. Finally, several test cases are described that illustrate recent progress made in this field. The goal of this publication is to provide an understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods.

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• Multi-condition multi-objective optimization using deep reinforcement learning
• Reinforcement-learning-based control of convectively-unstable flows
• Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections
• Investigation of reinforcement learning for shape optimization of profile extrusion dies
• How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
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• Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate