A review on deep reinforcement learning for fluid mechanics: an update

TL;DR

This paper provides a comprehensive review of deep reinforcement learning applications in fluid mechanics, analyzing recent advances, methodologies, and future directions across various subfields like flow control and shape optimization.

Contribution

It offers an updated, detailed synthesis of existing literature, comparing algorithmic choices and technical implementations in DRL for fluid mechanics, highlighting current trends and future prospects.

Findings

DRL techniques are increasingly applied in fluid mechanics problems.

Algorithmic choices significantly impact DRL performance in fluid applications.

The review identifies key challenges and potential future research directions.

Abstract

In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning (DRL) techniques has increased at fast pace, leading to a growing bibliography on the topic. While the capabilities of DRL to solve complex decision-making problems make it a valuable tool for active flow control, recent publications also demonstrated applications to other fields, such as shape optimization or microfluidics. The present work aims at proposing an exhaustive review of the existing literature, and is a follow-up to our previous review on the topic. The contributions are regrouped by field of application, and are compared together regarding algorithmic and technical choices, such as state selection, reward design, time granularity, and more. Based on these comparisons, general conclusions are drawn regarding the current state-of-the-art in the domain, and perspectives for future improvements are sketched.