Active flow control using machine learning: A brief review

Feng Ren, Haibao Hu, Hui Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines, especially those involving big data. Successes in these areas also attract researchers from the community of fluid mechanics, especially in the field of active flow control (AFC). This article surveys recent successful applications of machine learning in AFC, highlights general ideas, and aims at offering a basic outline for those who are interested in this specific topic. In this short review, we focus on two methodologies, i.e., genetic programming (GP) and deep reinforcement learning (DRL), both having been proven effective, efficient, and robust in certain AFC problems, and outline some future prospects that might shed some light for relevant studies.

Original languageEnglish
Pages (from-to)247-253
Number of pages7
JournalJournal of Hydrodynamics
Issue number2
Publication statusPublished - 1 Apr 2020


  • Active flow control (AFC)
  • deep reinforcement learning (DRL)
  • genetic programming (GP)
  • machine learning

ASJC Scopus subject areas

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


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