Dynamical Model

Dynamical modeling focuses on creating mathematical representations of systems that evolve over time, aiming to predict future states and understand underlying mechanisms. Current research emphasizes developing data-driven models using neural networks, including transformers, recurrent networks, and physics-informed neural networks, often coupled with techniques like Koopman operator theory and manifold learning to handle high-dimensional or noisy data. These advancements improve the accuracy and efficiency of dynamical models across diverse fields, from robotics and fluid dynamics to biophysics and traffic flow, enabling better predictions and control of complex systems. The development of robust and interpretable models remains a key challenge and area of active investigation.