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 …

Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift
increase, mixing enhancement, and noise reduction. Current and future applications have …

We present the first application of an artificial neural network trained through a deep
reinforcement learning agent to perform active flow control. It is shown that, in a two …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

In the present work, we assess the capabilities of neural networks to predict temporally
evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehlis …

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 …

I. Nomenclature α, AoA= angle of attack ρ= raw pixel density ρ= pixel density c= chord length
Cl= sectional lift coefficient CFD= computational fluid dynamics CNN= convolutinal neural …

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 present a single-layer feed-forward artificial neural network architecture trained through
a supervised learning approach for the deconvolution of flow variables from their coarse …

Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to
discover complex active flow control strategies [Rabault et al.,“Artificial neural networks …