Kinetic Model

Kinetic models mathematically describe the rates of change in systems, aiming to predict their future states. Current research focuses on developing efficient and accurate kinetic models across diverse fields, employing techniques like neural networks, variational autoencoders, and novel numerical methods (e.g., FORCE) to improve model fidelity and reduce computational cost. These advancements are impacting various domains, from robotics and computer vision (e.g., optical flow estimation) to biomedical image analysis and the optimization of complex chemical reactions, enabling more accurate predictions and improved system control.