Add to ORKGIn the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning (DRL) techniques has increased at fast pace, leading to a growing bibliography on the topic. Due to its ability to solve complex decision-making problems, DRL has especially emerged as a valuable tool to perform flow control, but recent publications also advertise great potential for other applications, such as shape optimization or micro-fluidics. The present work proposes an exhaustive review of the existing literature, and is a follow-up to our previous review on the topic. The contributions are regrouped by domain of application, and are compared together regarding algorithmic and technical choices, such as state selection, reward ...
Deep Reinforcement Learning (DRL) recently led to new control solutions for dynamic systems across v...
This paper focuses on the active flow control of a computational fluid dynamics simulation over a ra...
International audienceThe field of fluid mechanics is rapidly advancing, driven by unprecedentedvolu...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
In the past couple of years, the interest of the fluid mechanics community for deep reinforcement le...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
This thesis evaluates the potential of novel reinforcement learning methods applied to flow control....
International audienceThis research gauges the capabilities of deep reinforcement learning (DRL) tec...
Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to discover complex ac...
We apply deep reinforcement learning (DRL) to reduce and increase the drag of a 2-dimensional wake f...
We propose an open-source python platform for applications of Deep Reinforcement Learning (DRL) in f...
Machine learning has recently become a promising technique in fluid mechanics, especially for active...
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and eng...
Active flow control has the potential of achieving remarkable drag reductions in applications for fl...
This thesis presents and evaluates an approach for model-based deep reinforcement learning used for ...
Deep Reinforcement Learning (DRL) recently led to new control solutions for dynamic systems across v...
This paper focuses on the active flow control of a computational fluid dynamics simulation over a ra...
International audienceThe field of fluid mechanics is rapidly advancing, driven by unprecedentedvolu...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
In the past couple of years, the interest of the fluid mechanics community for deep reinforcement le...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
This thesis evaluates the potential of novel reinforcement learning methods applied to flow control....
International audienceThis research gauges the capabilities of deep reinforcement learning (DRL) tec...
Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to discover complex ac...
We apply deep reinforcement learning (DRL) to reduce and increase the drag of a 2-dimensional wake f...
We propose an open-source python platform for applications of Deep Reinforcement Learning (DRL) in f...
Machine learning has recently become a promising technique in fluid mechanics, especially for active...
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and eng...
Active flow control has the potential of achieving remarkable drag reductions in applications for fl...
This thesis presents and evaluates an approach for model-based deep reinforcement learning used for ...
Deep Reinforcement Learning (DRL) recently led to new control solutions for dynamic systems across v...
This paper focuses on the active flow control of a computational fluid dynamics simulation over a ra...
International audienceThe field of fluid mechanics is rapidly advancing, driven by unprecedentedvolu...