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

C Vignon, J Rabault, R Vinuesa - Physics of fluids, 2023 - pubs.aip.org
Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

Review of active control of circular cylinder flow

WL Chen, Y Huang, C Chen, H Yu, D Gao - Ocean Engineering, 2022 - Elsevier
Fluid flow around a circular cylinder is ubiquitous in nature and in various industrial
applications. The periodic von Kármán vortex shedding from the cylinder is one of the …

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

H Tang, J Rabault, A Kuhnle, Y Wang, T Wang - Physics of Fluids, 2020 - pubs.aip.org
This paper focuses on the active flow control of a computational fluid dynamics simulation
over a range of Reynolds numbers using deep reinforcement learning (DRL). More …

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

F Ren, J Rabault, H Tang - Physics of Fluids, 2021 - pubs.aip.org
Machine learning has recently become a promising technique in fluid mechanics, especially
for active flow control (AFC) applications. A recent work [Rabault et al., J. Fluid Mech. 865 …

DRLinFluids: An open-source Python platform of coupling deep reinforcement learning and OpenFOAM

Q Wang, L Yan, G Hu, C Li, Y ** artificial intelligence has become a key solution for
problems of diverse disciplines, especially those involving big data. Successes in these …