Continuous Time

Continuous-time modeling focuses on representing and analyzing systems that evolve continuously over time, rather than in discrete steps. Current research emphasizes developing neural network architectures and algorithms, such as continuous-time recurrent neural networks (CTRNNs) and neural ordinary differential equations (NODEs), to handle irregularly sampled data and complex dynamics in various applications. This approach offers advantages in modeling real-world phenomena with inherent continuous-time behavior, improving accuracy and efficiency in areas like signal processing, control systems, and causal inference. The resulting advancements have significant implications for fields ranging from robotics and healthcare to finance and climate science.