Continuous Time Dynamical System

Continuous-time dynamical systems model the evolution of systems over continuous time, aiming to understand and predict their behavior. Current research focuses on developing efficient algorithms for control synthesis, optimization, and system identification within these systems, often employing techniques like reinforcement learning, Hamilton-Jacobi reachability analysis, and Koopman operator theory to handle nonlinearity and high dimensionality. These advancements are impacting diverse fields, enabling improved control strategies for complex systems (e.g., robotics, ecology) and more accurate data-driven modeling of physical processes through techniques like Gaussian process dynamics. The development of robust and efficient methods for analyzing and controlling these systems is crucial for advancing numerous scientific and engineering disciplines.