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.

中文翻译：

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使用机器学习的主动流控制：简要回顾

如今，快速发展的人工智能已成为解决各种学科（尤其是涉及大数据的学科）问题的关键解决方案。在这些领域的成功也吸引了流体力学领域的研究人员，特别是在主动流控制（AFC）领域。本文对AFC中机器学习的最新成功应用进行了调查，重点介绍了一般思路，旨在为对这一特定主题感兴趣的人提供基本的概述。在这篇简短的评论中，我们将重点介绍两种方法，即基因编程（GP）和深度强化学习（DRL），它们在某些AFC问题中均被证明是有效，高效和强大的，并概述了可能摆脱某些前景的一些前景有关研究的光。