Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization

TL;DR

This paper reviews how Deep Reinforcement Learning (DRL) is emerging as a promising approach for solving complex control and shape optimization problems in fluid mechanics, outperforming traditional methods in handling nonlinearity and high dimensionality.

Contribution

It provides an overview of the current state of DRL applications in fluid mechanics, highlighting its potential for active flow control and shape optimization.

Findings

DRL shows promise in solving complex fluid control problems.

Deep learning methods can handle high-dimensional fluid dynamics data.

Initial results indicate improved efficiency over traditional techniques.

Abstract

In recent years, Artificial Neural Networks (ANNs) and Deep Learning have become increasingly popular across a wide range of scientific and technical fields, including Fluid Mechanics. While it will take time to fully grasp the potentialities as well as the limitations of these methods, evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known. This is particularly true in Fluid Mechanics, where problems involving optimal control and optimal design are involved. Indeed, such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity, non convexity, and high dimensionality they involve. By contrast, Deep Reinforcement Learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. In this short review, we offer an insight into the current state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems.