English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep reinforcement learning (DRL) has recently been adopted in a wide rangeof physics and engineering domains for its ability to solve decision-makingproblems that were previously out of reach due to a combination ofnon-linearity and high dimensionality. In the last few years, it has spread inthe field of computational mechanics, and particularly in fluid dynamics, withrecent applications in flow control and shape optimization. In this work, weconduct a detailed review of existing DRL applications to fluid mechanicsproblems. In addition, we present recent results that further illustrate thepotential of DRL in Fluid Mechanics. The coupling methods used in each case arecovered, detailing their advantages and limitations. Our review also focuses onthe comparison with classical methods for optimal control and optimization.Finally, several test cases are described that illustrate recent progress madein this field. The goal of this publication is to provide an understanding ofDRL capabilities along with state-of-the-art applications in fluid dynamics toresearchers wishing to address new problems with these methods.

A review on Deep Reinforcement Learning for Fluid Mechanics