Temporal Evolution

Temporal evolution analysis focuses on understanding how systems change over time, aiming to predict future states or classify dynamic behaviors. Current research employs diverse approaches, including deep learning models like diffusion-based morphing and historical information passing networks, as well as physics-informed neural networks and graph-based methods for analyzing dynamic systems and aggregated data. These advancements are impacting fields ranging from medical image analysis and epidemiological modeling to traffic flow prediction and the characterization of disadvantaged communities, enabling more accurate predictions and improved understanding of complex dynamic processes.