Koopman Operator Theory Based Machine Learning of Dynamical Systems

Explore the cutting-edge application of Koopman Operator Theory (KOT) to machine learning for dynamical systems in this informative lecture. Delve into the challenges faced by traditional machine learning approaches when dealing with complex process dynamics, and discover how KOT offers a solution inspired by human intelligence. Learn about the mathematical foundations of KOT and its ability to create generative, predictive, and context-aware models adaptable to feedback control applications. Gain insights into computational methods that enable efficient processing, and examine real-world applications in fluid dynamics, power grid dynamics, network security, soft robotics, and game dynamics. This talk, presented by Igor Mezic from the University of California, Santa Barbara, provides a comprehensive overview of KOT-based machine learning and its potential to revolutionize our understanding and control of complex dynamical systems.