Complex Dynamical System

Complex dynamical systems research aims to understand and predict the behavior of intricate systems exhibiting nonlinear interactions and often chaotic dynamics. Current efforts focus on developing data-driven models, employing architectures like neural ordinary differential equations (NODEs), Koopman operators, and physics-informed neural networks (PINNs), often enhanced with techniques like Bayesian methods and mixed-integer optimization for improved accuracy and robustness. These advancements are crucial for tackling challenges in diverse fields, including climate modeling, control systems engineering, and biological systems analysis, by enabling more accurate predictions and a deeper understanding of complex system behavior.