Deep Dynamic

Deep dynamic modeling focuses on learning and representing the temporal evolution of complex systems using neural networks. Current research emphasizes developing stable and physically consistent models, often employing neural ordinary differential equations (NODEs) or incorporating principles like dissipativity and Koopman theory, to improve prediction accuracy and generalization across diverse applications. These advancements are significant for various fields, enabling more accurate predictions in areas such as robotics, autonomous driving, fluid dynamics, and epidemiological modeling, ultimately leading to improved control and decision-making capabilities.