Reachability of Black-Box Nonlinear Systems after Koopman Operator Linearization
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Abstract (arXiv)
Reachability analysis of nonlinear dynamical systems is a challenging and computationally expensive task. Computing the reachable states for linear systems, in contrast, can often be done efficiently in high dimensions. In this paper, we explore verification methods that leverage a connection between these two classes of systems based on the concept of the Koopman operator. The Koopman operator links the behaviors of a nonlinear system to a linear system embedded in a higher dimensional space, with an additional set of so-called observable variables. Although, the new dynamical system has linear differential equations, the set of initial states is defined with nonlinear constraints. For this reason, existing approaches for linear systems reachability cannot be used directly. In this paper, we propose the first reachability algorithm that deals with this unexplored type of reachability problem. Our evaluation examines several optimizations, and shows the proposed workflow is a promising avenue for verifying behaviors of nonlinear systems.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Reachability Analysis of a General Class of Neural Ordinary Differential Equationscited by 13
- 2026Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guaranteescited by 1
- 2021Data-driven Set-based Estimation of Polynomial Systems with Application to SIR Epidemicscited by 0
- 2023On the Trade-off Between Efficiency and Precision of Neural Abstractioncited by 0
- 2026Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees3 Jan 2026
- 2023On the Trade-off Between Efficiency and Precision of Neural Abstraction28 Jul 2023
- 2022Reachability Analysis of a General Class of Neural Ordinary Differential Equations13 Jul 2022
- 2021Data-driven Set-based Estimation of Polynomial Systems with Application to SIR Epidemics8 Nov 2021