The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

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

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

We develop a new method which extends dynamic mode decomposition (DMD) to
incorporate the effect of control to extract low-order models from high-dimensional, complex …

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 …

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 …

A critical challenge in the data-driven modeling of dynamical systems is producing methods
robust to measurement error, particularly when data is limited. Many leading methods either …

In this study, the authors present an overview of closed‐loop subspace identification
methods found in the recent literature. Since a significant number of algorithms has …

A few years ago the present authors launched a new approach to parametric identification of
linear continuous-time systems [11]. Its main features may be summarised as follows: closed …