Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization

Jean Rabault, Feng Ren, Wei Zhang, Hui Tang, Hui Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)234-246
Number of pages13
JournalJournal of Hydrodynamics
Issue number2
Publication statusPublished - 1 Apr 2020


  • deep reinforcement learning (DRL)
  • flow control
  • Machine learning

ASJC Scopus subject areas

  • Modelling and Simulation
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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