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 …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and
weather prediction, hinge on the choice of turbulence models. The abundance of data from …

Fish in schooling formations navigate complex flow fields replete with mechanical energy in
the vortex wakes of their companions. Their schooling behavior has been associated with …

Turbulent flow models are critical for applications such as aircraft design, weather
forecasting and climate prediction. Existing models are largely based on physical insight …

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 …

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 …

This review paper presents an overview of Vortex Methods for flow simulation and their
different sub-approaches, from their creation to the present. Particle methods distinguish …

Machine learning frameworks such as genetic programming and reinforcement learning
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …

In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …