Continuous Time Model

Continuous-time models offer a powerful framework for analyzing dynamic systems by representing their evolution as continuous processes rather than discrete steps. Current research focuses on applying these models to diverse fields, leveraging architectures like neural ordinary differential equations (NODEs), stochastic differential equations (SDEs), and neural networks to solve partial differential equations (PDEs) and model complex systems such as economic models, disease progression, and crowd dynamics. This approach allows for more accurate modeling of irregularly sampled data and improved handling of high-dimensional problems, leading to advancements in areas like time series forecasting, causal inference, and control systems. The resulting improvements in accuracy and interpretability are driving significant progress across various scientific disciplines and practical applications.