  Formal verification techniques play a pivotal role in ensuring the safety of
complex cyber-physical systems. To transfer model-based verification results to
the real world, we require that the measurements of the target system lie in
the set of reachable outputs of the corresponding model, a property we refer to
as reachset conformance. This paper is on automatically identifying those
reachset-conformant models. While state-of-the-art reachset-conformant
identification methods focus on linear state-space models, we generalize these
methods to nonlinear state-space models and linear and nonlinear input-output
models. Furthermore, our identification framework adapts to different levels of
prior knowledge on the system dynamics. In particular, we identify the set of
model uncertainties for white-box models, the parameters and the set of model
uncertainties for gray-box models, and entire reachset-conformant black-box
models from data. For the black-box identification, we propose a new genetic
programming variant, which we call conformant genetic programming. The
robustness and efficacy of our framework are demonstrated in extensive
numerical experiments using simulated and real-world data.
