Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

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 …

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow
control problems both in experiments and simulations by automatically discovering active …

Effective thermal management is critical for optimising device performance, extending
product longevity, saving energy, protecting the environment, and avoiding thermal failures …

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 recent years, artificial neural networks (ANNs) and deep learning have become
increasingly popular across a wide range of scientific and technical fields, including fluid …

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 …

Shape optimization is an indispensable step in any aerodynamic design. However, the
inherent complexity and non-linearity associated with fluid mechanics as well as the high …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

This paper applies deep reinforcement learning (DRL) on the synthetic jet control of flows
over an NACA (National Advisory Committee for Aeronautics) 0012 airfoil under weak …