Interval Reachability

Interval reachability analysis aims to rigorously determine the set of all possible states a system can reach, considering uncertainties in inputs and parameters. Current research heavily focuses on applying this technique to systems incorporating neural networks, particularly addressing challenges posed by nonlinear dynamics and closed-loop feedback control, often employing mixed monotonicity and contraction-based methods to improve computational efficiency and accuracy. These advancements are crucial for verifying the safety and robustness of increasingly complex systems, such as those controlled by AI, and have implications for various fields including robotics, autonomous driving, and aerospace engineering.