Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS 2021), Virtually, July 7-9, 2021


This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models. Although techniques exist for the validation of many classes of models, the majority of these methods cannot be applied to the whole of Lipschitz-continuous models, which includes neural network models. Additionally, existing techniques generally consider only white-box models. By taking a sampling-based approach, we can address black-box models, represented only by an input-output relationship and a Lipschitz constant. We show that by randomly sampling from a parameter space and evaluating the model, it is possible to guarantee the correctness of traces labeled consistent and provide a confidence on the correctness of traces labeled inconsistent. We evaluate the applicability and scalability of ModelGuard in three case studies, including a physical platform.

Subject Area

CPS Safe Autonomy

Publication Source

7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS 2021)


model invalidation, neural network, computational tool, monitoring



Date Posted: 19 November 2021

This document has been peer reviewed.