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 …

Natural ventilation nowadays has been paid great concerns due to its zero carbon emission
and good performance on the human health. In engineering applications, cross ventilation …

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 …

Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit
multiple complex phenomena in both time and space. Reduced Order Modeling (ROM) of …

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 …

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 …

This paper focuses on finding a closed-loop strategy to reduce the drag of a cylinder in
laminar flow conditions. Deep reinforcement learning algorithms have been implemented to …

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 …

We mitigate vortex-induced vibrations of a square cylinder at a Reynolds number of 100
using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed …

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 …