# Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction

arXiv: https://arxiv.org/abs/2003.11662 · HTML: https://mlsanity.com/paper/2003.11662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [ModelGuard: Runtime Validation of Lipschitz-continuous Models](https://mlsanity.com/paper/2104.15006/) (2021)
- [Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference](https://mlsanity.com/paper/2306.09966/) (2023)

## Abstract (arXiv)

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.