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

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 …

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 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 …

This paper presents an active learning strategy for robotic systems that takes into account
task information, enables fast learning, and allows control to be readily synthesized by …

In many applications it is important to estimate a fluid flow field from limited and possibly
corrupt measurements. Current methods in flow estimation often use least squares …

This paper presents a structure-exploiting nonlinear model reduction method for systems
with general nonlinearities. First, the nonlinear model is lifted to a model with more structure …

This paper proposes a supervised machine learning framework for the non-intrusive model
order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state …

This paper presents a physics-based data-driven method to learn predictive reduced-order
models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a …

This paper presents a randomized algorithm for computing the near-optimal low-rank
dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to …