Data-driven Reachability Verification with Probabilistic Guarantees under Koopman Spectral Uncertainty

Abstract

Providing rigorous reachability guarantees for unknown complex systems is a crucial and challenging task. In this paper, we present a novel data-driven framework that addresses this challenge by leveraging Koopman operator theory. Instead of operating in the state space, the proposed method encodes model uncertainty from finite data directly into Koopman spectral representation with quantifiable error bounds. Leveraging this spectral information, we systematically determine time intervals within which trajectories from the initial set are guaranteed, with a prescribed probability, to reach the target set. We finally demonstrate the efficacy of our framework in numerical examples.