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 stabilize the flow past a cluster of three rotating cylinders–the fluidic pinball–with
automated gradient-enriched machine learning algorithms. The control laws command the …

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 …

A comparative assessment of machine-learning (ML) methods for active flow control is
performed. The chosen benchmark problem is the drag reduction of a two-dimensional …

We address a challenge of active flow control: the optimization of many actuation
parameters guaranteeing fast convergence and avoiding suboptimal local minima. This …

Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in
the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient …

This chapter aims to give an overview of recent applications of Evolutionary Machine
Learning (EML) to control including opportunities and challenges. Control is at the heart of …

ABSTRACTFlow control has a tremendous technological and economic impact, such as
aerodynamic drag reduction on road vehicles which translates directly into fuel savings, with …

We stabilize the flow past a cluster of three rotating cylinders–the fluidic pinball–with
automated gradient-enriched machine learning algorithms. The control laws command the …

This paper presents the application of explorative gradient method (EGM)(Li et al., 2022) to
three open-loop flow control benchmarks with multiple actuators. The fluidic pinball with …