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 …

Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics
and engineering domains for its ability to solve decision-making problems that were …

Two deep learning (DL) models addressing the super-resolution (SR) reconstruction of
turbulent flows from low-resolution coarse flow field data are developed. One is the static …

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 …

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 the last years, supervised learning (SL) has established itself as the state-of-the-art for
data-driven turbulence modeling. In the SL paradigm, models are trained based on a …

Abstract Deep Reinforcement Learning (DRL) has recently spread into a range of domains
within physics and engineering, with multiple remarkable achievements. Still, much remains …

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 …

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 …

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 …