Complex Dynamic

Complex dynamic systems research focuses on understanding and modeling the intricate, often unpredictable behavior of systems with many interacting components. Current efforts concentrate on developing data-driven methods, employing architectures like neural ordinary differential equations (NODEs), recurrent neural networks (RNNs), graph neural networks (GNNs), and variational autoencoders (VAEs), to learn and predict these dynamics from observational data, often incorporating techniques like Koopman operator theory and dynamic mode decomposition (DMD). This work is crucial for advancing fields ranging from molecular biology and fluid dynamics to robotics and economics, enabling more accurate predictions, improved control strategies, and deeper insights into the underlying mechanisms governing complex systems.