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

Active control of laminar boundary layers with dielectric barrier discharge (DBD) plasma
actuators (PAs) has made considerable progress in the last 15 years. First pioneering …

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 …

The sparse identification of nonlinear dynamics (SINDy) is a recently proposed data-driven
modelling framework that uses sparse regression techniques to identify nonlinear low-order …

We propose a general dynamic reduced-order modelling framework for typical experimental
data: time-resolved sensor data and optional non-time-resolved particle image velocimetry …

Organized structures in turbulent jets can be modeled as wavepackets. These are
characterized by spatial amplification and decay, both of which are related to stability …

Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system
governed by the one-dimensional Kuramoto–Sivashinsky (KS) equation. DRL uses …

Research in modern data-driven dynamical systems is typically focused on the three key
challenges of high dimensionality, unknown dynamics and nonlinearity. The dynamic mode …

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 …

Identifying accurate and yet interpretable low-order models from data has gained a renewed
interest over the past decade. In the present work, we illustrate how the combined use of …