Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions

نویسندگان

چکیده

Deep reinforcement learning (DRL) has been applied to a variety of problems during the past decade and provided effective control strategies in high-dimensional non-linear situations that are challenging traditional methods. Flourishing applications now spread out into field fluid dynamics specifically active flow (AFC). In community AFC, encouraging results obtained two-dimensional chaotic conditions have raised interest study increasingly complex flows. this review, we first provide general overview reinforcement-learning DRL frameworks, as well their recent advances. We then focus on application highlighting current limitations algorithms field, suggesting some potential upcoming milestones reach, open questions likely attract attention mechanics community.

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ژورنال

عنوان ژورنال: Physics of Fluids

سال: 2023

ISSN: ['1527-2435', '1089-7666', '1070-6631']

DOI: https://doi.org/10.1063/5.0143913