Data-driven property verification of grey-box systems by bayesian experiment design

A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of optimal experiments. Applied to dynamical systems, this work enables data-driven verification of partly-known system dynamics with controllable non-determinism (inputs) and noisy output observations. A numerical case study concerning the safety of a dynamical system is used to elucidate this data-driven and model-based verification technique.