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 …

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 …

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 …

A comprehensive review of recent advancements in applying deep reinforcement learning
(DRL) to fluid dynamics problems is presented. Applications in flow control and shape …

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big
data is a concept currently in its infancy which requires further development. It is of special …

The application of deep reinforcement learning (DRL) to train an agent capable of learning
control laws for pulsed jets to manipulate the wake of a bluff body is presented and …

This work reports the application of a model-free deep reinforcement learning (DRL) based
flow control strategy to suppress perturbations evolving in the one-dimensional linearised …

We propose a Cluster-Based Control (CBC) strategy for model-free feedback drag reduction
with multiple actuators and full-state feedback. CBC consists of three steps. First, the input of …

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML)
algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise …

As a promising machine learning method for active flow control (AFC), deep reinforcement
learning (DRL) has been successfully applied in various scenarios, such as the drag …