Dynamic Modeling

Dynamic modeling focuses on creating mathematical representations of systems that change over time, aiming to predict their behavior and facilitate control. Current research emphasizes developing differentiable dynamic models, often incorporating deep learning architectures like neural networks and neural ordinary differential equations (NODEs), for improved efficiency in optimization and control tasks, particularly in robotics and complex systems. These advancements are crucial for applications ranging from autonomous robot navigation and control to predictive modeling in fields like oncology and mechanical engineering, enabling more accurate simulations, optimized designs, and improved system performance.