# Reachset-Conformant System Identification

arXiv: https://arxiv.org/abs/2407.11692 · HTML: https://mlsanity.com/paper/2407.11692/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 5 of 5)

- [Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks](https://mlsanity.com/paper/2508.11025/) (2025)
- [Leveraging Analytic Gradients in Provably Safe Reinforcement Learning](https://mlsanity.com/paper/2506.01665/) (2025)
- [Training Verifiably Robust Agents Using Set-Based Reinforcement Learning](https://mlsanity.com/paper/2408.09112/) (2024)
- [To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control](https://mlsanity.com/paper/2604.11463/) (2026)
- [Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting](https://mlsanity.com/paper/2605.12341/) (2026)

## Abstract (arXiv)

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. The robustness and efficacy of our framework are demonstrated in extensive numerical experiments using simulated and real-world data.