Koopman Operator Theory Based Machine Learning of Dynamical Systems

Koopman Operator Theory Based Machine Learning of Dynamical Systems

Explore Koopman operator theory and its applications in machine learning for dynamical systems in this lecture by Igor Mezic from the University of California. Delivered as part of the Third Symposium on Machine Learning and Dynamical Systems at the Fields Institute, delve into advanced concepts at the intersection of operator theory and data-driven modeling. Gain insights into how Koopman operators can enhance understanding and prediction of complex dynamical systems, with potential applications across various scientific and engineering domains.