Add to ORKGWe 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 nonlinear and high-dimensional problems. Here, an agent maximizes a cumulative reward with learning a feedback policy by acting in an environment. In control theory terms, the cumulative reward would correspond to the cost function, the agent to the actuator, the environment to the measured signals and the learned policy to the feedback law. Thus, DRL assumes an interactive environment or, equivalently, control plant. The setup of a numerical simulation plant with DRL is challenging and time-consuming. In this work, a novel python platform, named DRLinFluids is develope...
This thesis evaluates the potential of novel reinforcement learning methods applied to flow control....
For active flow control, flow around a 2D cylinder is considered a generic example. The von kármán v...
International audiencenstabilities arise in a number of flow configurations. One such manifestation ...
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...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
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...
This paper focuses on the active flow control of a computational fluid dynamics simulation over a ra...
Deep Reinforcement Learning (DRL) recently led to new control solutions for dynamic systems across v...
Machine learning has recently become a promising technique in fluid mechanics, especially for active...
Active flow control has the potential of achieving remarkable drag reductions in applications for fl...
Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dyn...
This thesis presents and evaluates an approach for model-based deep reinforcement learning used for ...
International audienceThis research gauges the capabilities of deep reinforcement learning (DRL) tec...
This thesis evaluates the potential of novel reinforcement learning methods applied to flow control....
For active flow control, flow around a 2D cylinder is considered a generic example. The von kármán v...
International audiencenstabilities arise in a number of flow configurations. One such manifestation ...
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...
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineeri...
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...
This paper focuses on the active flow control of a computational fluid dynamics simulation over a ra...
Deep Reinforcement Learning (DRL) recently led to new control solutions for dynamic systems across v...
Machine learning has recently become a promising technique in fluid mechanics, especially for active...
Active flow control has the potential of achieving remarkable drag reductions in applications for fl...
Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dyn...
This thesis presents and evaluates an approach for model-based deep reinforcement learning used for ...
International audienceThis research gauges the capabilities of deep reinforcement learning (DRL) tec...
This thesis evaluates the potential of novel reinforcement learning methods applied to flow control....
For active flow control, flow around a 2D cylinder is considered a generic example. The von kármán v...
International audiencenstabilities arise in a number of flow configurations. One such manifestation ...