TIRA: Toolbox for Interval Reachability Analysis
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Abstract (arXiv)
This paper presents TIRA, a Matlab library gathering several methods for the computation of interval over-approximations of the reachable sets for both continuous- and discrete-time nonlinear systems. Unlike other existing tools, the main strength of interval-based reachability analysis is its simplicity and scalability, rather than the accuracy of the over-approximations. The current implementation of TIRA contains four reachability methods covering wide classes of nonlinear systems, handled with recent results relying on contraction/growth bounds and monotonicity concepts. TIRA's architecture features a central function working as a hub between the user-defined reachability problem and the library of available reachability methods. This design choice offers increased extensibility of the library, where users can define their own method in a separate function and add the function call in the hub function.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Feb 2019.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 5; 2024: 0; 2025: 1; 2026: 1.
12 papers.
- 2021GoTube: Scalable Stochastic Verification of Continuous-Depth Modelscited by 12
- 2021Learning Density Distribution of Reachable States for Autonomous Systemscited by 11
- 2023Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllerscited by 9
- 2023Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loopscited by 9
- 2023A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systemscited by 6
- 2021Provably Correct Training of Neural Network Controllers Using Reachability Analysiscited by 5
- 2025Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiencycited by 2
- 2021Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approachcited by 1
- 2026TNODEV: Toolbox for Neural ODE Verificationcited by 1
- 2022Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attackscited by 0
- 2023Convex Hulls of Reachable Setscited by 0
- 2023Robustness Analysis of Continuous-Depth Models with Lagrangian Techniquescited by 0
- 2026TNODEV: Toolbox for Neural ODE Verification15 Jun 2026
- 2025Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency15 Oct 2025
- 2023Robustness Analysis of Continuous-Depth Models with Lagrangian Techniques23 Aug 2023
- 2023Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops27 Jul 2023
- 2023A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems27 Jun 2023
- 2023Convex Hulls of Reachable Sets30 Mar 2023
- 2023Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers19 Jan 2023
- 2022Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks28 Dec 2022
- 2021Learning Density Distribution of Reachable States for Autonomous Systems14 Sep 2021
- 2021Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach3 Sep 2021
- 2021GoTube: Scalable Stochastic Verification of Continuous-Depth Models18 Jul 2021
- 2021Provably Correct Training of Neural Network Controllers Using Reachability Analysis22 Feb 2021