  We consider data-driven reachability analysis of discrete-time stochastic
dynamical systems using conformal inference. We assume that we are not provided
with a symbolic representation of the stochastic system, but instead have
access to a dataset of $K$-step trajectories. The reachability problem is to
construct a probabilistic flowpipe such that the probability that a $K$-step
trajectory can violate the bounds of the flowpipe does not exceed a
user-specified failure probability threshold. The key ideas in this paper are:
(1) to learn a surrogate predictor model from data, (2) to perform reachability
analysis using the surrogate model, and (3) to quantify the surrogate model's
incurred error using conformal inference in order to give probabilistic
reachability guarantees. We focus on learning-enabled control systems with
complex closed-loop dynamics that are difficult to model symbolically, but
where state transition pairs can be queried, e.g., using a simulator. We
demonstrate the applicability of our method on examples from the domain of
learning-enabled cyber-physical systems.
