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

Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …

Koopman operators are of infinite dimension and capture the characteristics of nonlinear
dynamics in a lifted global linear manner. The finite data-driven approximation of Koopman …

The striking fractal geometry of strange attractors underscores the generative nature of
chaos: like probability distributions, chaotic systems can be repeatedly measured to produce …

Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

We present a method to design a state-feedback controller ensuring exponential stability for
nonlinear systems using only measurement data. Our approach relies on Koopman-operator …

For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a
popular method to obtain data-driven surrogate models. Its theoretical foundation is the …

Koopman-based modeling and model predictive control have been a promising alternative
for optimal control of nonlinear processes. Good Koopman modeling performance …

Dynamical systems provide a comprehensive way to study complex and changing behaviors
across various sciences. Many modern systems are too complicated to analyze directly or …

Many machine learning approaches for decision making, such as reinforcement learning,
rely on simulators or predictive models to forecast the time-evolution of quantities of interest …