A review on Deep Reinforcement Learning for Fluid Mechanics

TL;DR

This paper reviews the application of deep reinforcement learning in fluid mechanics, highlighting recent advancements, coupling methods, and comparisons with classical approaches, to guide researchers in leveraging DRL for complex fluid dynamics problems.

Contribution

It provides a comprehensive review of DRL applications in fluid mechanics, including recent results, coupling techniques, and case studies, advancing understanding of DRL's potential in this field.

Findings

DRL shows promise in flow control and shape optimization.

Coupling methods have specific advantages and limitations.

Recent progress demonstrates DRL's effectiveness in fluid dynamics tasks.

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.