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 …

Static and dynamic metamaterials have been extensively studied for their ability to
manipulate different physical fields and directed to broad applications. Because the …

Physics-informed neural networks (PINNs) are successful machine-learning methods for the
solution and identification of partial differential equations. We employ PINNs for solving the …

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 …

Two models based on convolutional neural networks are trained to predict the two-
dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a …

Modal-decomposition techniques are computational frameworks based on data aimed at
identifying a low-dimensional space for capturing dominant flow features: the so-called …

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 work presents a review of the current state of research in data‐driven turbulence
closure modeling. It offers a perspective on the challenges and open issues but also on the …