Blog Posts

May 30, 2020
Koopman observable subspaces provide a unique way to represent a dynamical system that is particularly attractive for machine learning. Many physical systems exhibit extremely non-linear, multi-scale and chaotic phenomena which can be difficult to model and control. Koopman brings promises of being able to represent any dynamical system through linear dynamics. We explore the fundamentals of Koopman operators, the simplifications and challenges they bring to dynamical modeling, and how they can be exploited for developing machine learning models of physical systems.