A review on deep reinforcement learning for fluid mechanics

Paul Garnier, Jonathan Viquerat, Elie Hachem

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.