Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
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 …
past decade and has provided effective control strategies in high-dimensional and non …
A review on deep reinforcement learning for fluid mechanics: An update
J Viquerat, P Meliga, A Larcher, E Hachem - Physics of Fluids, 2022 - pubs.aip.org
In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …
reinforcement learning techniques has increased at fast pace, leading to a growing …
Deep reinforcement learning for turbulent drag reduction in channel flows
We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …
Automating turbulence modelling by multi-agent reinforcement learning
G Novati, HL de Laroussilhe… - Nature Machine …, 2021 - nature.com
Turbulent flow models are critical for applications such as aircraft design, weather
forecasting and climate prediction. Existing models are largely based on physical insight …
forecasting and climate prediction. Existing models are largely based on physical insight …
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
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 …
over a range of Reynolds numbers using deep reinforcement learning (DRL). More …
Reinforcement-learning-based control of confined cylinder wakes with stability analyses
This work studies the application of a reinforcement learning (RL)-based flow control
strategy to the flow past a cylinder confined between two walls to suppress vortex shedding …
strategy to the flow past a cylinder confined between two walls to suppress vortex shedding …
Applying deep reinforcement learning to active flow control in weakly turbulent conditions
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 …
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
We propose an open-source Python platform for applications of deep reinforcement learning
(DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in …
(DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in …
From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration
In the present work, an efficient active flow control strategy in eliminating vortex-induced
vibration of a cylinder at Re= 100 has been explored by two machine learning frameworks …
vibration of a cylinder at Re= 100 has been explored by two machine learning frameworks …
Flow control in wings and discovery of novel approaches via deep reinforcement learning
In this review, we summarize existing trends of flow control used to improve the aerodynamic
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …