Abstract
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.
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Acknowledgements
This work was support by the Research Grants Council of Hong Kong under General Research Fund (Grant Nos. 15249316, 15214418), the Departmental General Research Fund (Grant No. G-YBXQ).
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Ren, F., Hu, Hb. & Tang, H. Active flow control using machine learning: A brief review. J Hydrodyn 32, 247–253 (2020). https://doi.org/10.1007/s42241-020-0026-0
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DOI: https://doi.org/10.1007/s42241-020-0026-0