  In this paper, we consider the data-driven model invalidation problem for
Lipschitz continuous systems, where instead of given mathematical models, only
prior noisy sampled data of the systems are available. We show that this
data-driven model invalidation problem can be solved using a tractable
feasibility check. Our proposed approach consists of two main components: (i) a
data-driven abstraction part that uses the noisy sampled data to
over-approximate the unknown Lipschitz continuous dynamics with upper and lower
functions, and (ii) an optimization-based model invalidation component that
determines the incompatibility of the data-driven abstraction with a newly
observed length-T output trajectory. Finally, we discuss several methods to
reduce the computational complexity of the algorithm and demonstrate their
effectiveness with a simulation example of swarm intent identification.
