Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

We present the first application of an artificial neural network trained through a deep reinforcement learning agent to perform active flow control. It is shown that, in a two-dimensional simulation of the Kármán vortex street at moderate Reynolds number ( $Re=100$ ), our artificial neural network i...

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Bibliographic Details
Published in:Journal of fluid mechanics Vol. 865; pp. 281 - 302
Main Authors: Rabault, Jean, Kuchta, Miroslav, Jensen, Atle, Réglade, Ulysse, Cerardi, Nicolas
Format: Journal Article
Language:English
Published: Cambridge, UK Cambridge University Press 25.04.2019
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ISSN:0022-1120, 1469-7645
Online Access:Get full text
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