A review on Deep Reinforcement Learning for Fluid Mechanics

By Paul Garnier, Jonathan Viquerat, Jean Rabault, A. Larcher, Alexander Kuhnle, Elie Hachem. Computers & Fluids, 2019

330 citations recorded by the source corpus.

In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning techniques has increased at fast pace, leading to a growing bibliography on the topic. Due to its ability to solve complex decision-making problems, deep reinforcement learning has especially emerged as a valuable tool to perform flow control, but recent publications also advertise the great potential for other applications, such as shape optimization or microfluidics. The present work proposes an exhaustive review of the existing literature and is a follow-up to our previous review on the topic. The contributions are regrouped by the domain 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, and perspectives for future improvements are sketched.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6a8a95d429e1aade7bff06b0088a02f98a3e2396

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Ji-chao Li
2. Xiaosong Du
3. Joaquim R. Martins
4. Gaetan K. W. Kenway

In Statistical and Nonlinear Physics

1. Joaquim R. Martins
2. Ji-chao Li
3. Xiaosong Du

Citations in the corpus

• Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “[228], policy-based methods converge on optimal parameters more quickly and reliably than value-based RL.”