Koopman Model

Koopman operator theory provides a framework for analyzing nonlinear dynamical systems by representing them as linear operators in a higher-dimensional space, enabling the application of linear techniques to complex systems. Current research focuses on developing data-driven methods, such as Koopman autoencoders and extended dynamic mode decomposition (EDMD), often incorporating deep learning for efficient model construction and reduction, with a particular emphasis on improving long-term prediction accuracy and real-time applicability. This approach has significant implications for various fields, including robotics, process control (e.g., optimizing air separation units), and time series forecasting, offering improved model accuracy and computational efficiency for complex systems.