SIMPLE aerodynamic configurations under even modest conditions can exhibit complex
flows with a wide range of temporal and spatial features. It has become common practice in …

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 …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

We present the first application of an artificial neural network trained through a deep
reinforcement learning agent to perform active flow control. It is shown that, in a two …

Used to analyze the time-evolution of fluid flows, dynamic mode decomposition (DMD) has
quickly gained traction in the fluids community. However, the existing DMD literature focuses …

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 …

The description of coherent features of fluid flow is essential to our understanding of fluid-
dynamical and transport processes. A method is introduced that is able to extract dynamic …

The decomposition of experimental data into dynamic modes using a data-based algorithm
is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of …

Dynamic mode decomposition (DMD) has been extensively utilized to analyze the coherent
structures in many complex flows. Although specific flow patterns with dominant frequency …

This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems
are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit …