Temporal Domain

Temporal domain research focuses on modeling and analyzing systems that evolve over time, aiming to create predictive models robust to temporal variations in data distribution. Current efforts concentrate on developing algorithms and architectures, such as Koopman operators and deep reinforcement learning, to handle continuous-time dynamics, irregular data sampling, and the generalization of learned constraints across temporal instances. This work is crucial for advancing fields like knowledge graph completion, power distribution network optimization, and autonomous driving, where accurate prediction and robust performance in dynamic environments are paramount.