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20222025
most citedDeep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes

2 citations · 4 across the 5 of their papers we have counts for

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cs.CE2025★ 1 cited

Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000

R. Montalà, B. Font, P. Suárez +4

This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL…

cs.CE2025★ 1 cited

Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning

R. Montalà, B. Font, P. Suárez +4

In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack…