Applying deep reinforcement learning to active flow control in weakly turbulent conditions-Reference-Cited by-同舟云学术

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Applying deep reinforcement learning to active flow control in weakly turbulent conditions

Published:2021-03-01 Issue:3 Volume:33 Page:037121
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Ren Feng¹^ORCID,Rabault Jean²³^ORCID,Tang Hui¹^ORCID

Affiliation:

1. Research Center for Fluid Structure Interactions, Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China

2. Department of Information Technology, Norwegian Meteorological Institute, 0371 Oslo, Norway

3. Department of Mathematics, University of Oslo, 0851 Oslo, Norway

Funder

Research Grants Council, University Grants Committee

Norges Forskningsråd

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0037371

Reference39 articles.

1. Closed-loop separation control using machine learning

2. Artificial intelligence control of a turbulent jet

3. Active control of vortex-induced vibration of a circular cylinder using machine learning

4. Machine Learning for Fluid Mechanics

5. Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization

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