Advances in experimental techniques and the ever-increasing fidelity of numerical
simulations have led to an abundance of data describing fluid flows. This review discusses a …

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

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

Physics-informed neural networks (PINNs) are a recent trend in scientific machine learning
research and modeling of differential equations. Despite progress in PINN research, large …

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 …

Optimal sensor and actuator selection is a central challenge in high-dimensional estimation
and control. Nearly all subsequent control decisions are affected by these sensor and …

We extend the resolvent-based estimation approach recently introduced by Towne etal.(J.
Fluid Mech., vol. 883, 2020, A17) to obtain optimal, non-causal estimates of time-varying …

Transonic buffet is a phenomenon of aerodynamic instability with shock wave motions which
occurs at certain combinations of Mach number and mean angle of attack, and which limits …

Reynolds-averaged Navier–Stokes (RANS)-based data assimilation has proven to be
essential in many data-driven approaches, including the augmentation of experimental data …

This review article is concerned with the design of linear reduced-order models and control
laws for closed-loop control of instabilities in transitional flows. For oscillator flows, such as …