mlsanityData to 14 Oct 2026

JuliaReach: a Toolbox for Set-Based Reachability

arXiv 1901.10736 ↗First posted 30 Jan 2019eess.SY cs.SY math.DScited by 28

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Abstract (arXiv)

We present JuliaReach, a toolbox for set-based reachability analysis of dynamical systems. JuliaReach consists of two main packages: Reachability, containing implementations of reachability algorithms for continuous and hybrid systems, and LazySets, a standalone library that implements state-of-the-art algorithms for calculus with convex sets. The library offers both concrete and lazy set representations, where the latter stands for the ability to delay set computations until they are needed. The choice of the programming language Julia and the accompanying documentation of our toolbox allow researchers to easily translate set-based algorithms from mathematics to software in a platform-independent way, while achieving runtime performance that is comparable to statically compiled languages. Combining lazy operations in high dimensions and explicit computations in low dimensions, JuliaReach can be applied to solve complex, large-scale problems.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jan 2019.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 9; 2024: 8; 2025: 3; 2026: 2.

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  1. 2021Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopescited by 26
  2. 2022Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopescited by 24
  3. 2024Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programscited by 14
  4. 2022Reachability Analysis of a General Class of Neural Ordinary Differential Equationscited by 13
  5. 2025Neural Network Verification is a Programming Language Challengecited by 12
  6. 2023Neural Abstractionscited by 11
  7. 2023Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loopscited by 9
  8. 2023Shielded Reinforcement Learning for Hybrid Systemscited by 8
  9. 2024Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structurecited by 8
  10. 2024Scalable Surrogate Verification of Image-based Neural Network Control Systems using Composition and Unrollingcited by 8
  11. 2021Neural Predictive Monitoring under Partial Observabilitycited by 7
  12. 2024Provably Safe Neural Network Controllers via Differential Dynamic Logiccited by 7
16 more
  1. 2023Fully Automatic Neural Network Reduction for Formal Verificationcited by 6
  2. 2024Safe Reach Set Computation via Neural Barrier Certificatescited by 5
  3. 2024textttimmrax: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAXcited by 4
  4. 2023Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Inputcited by 2
  5. 2023Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guaranteescited by 2
  6. 2026Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllerscited by 2
  7. 2021Learning Provably Robust Motion Planners Using Funnel Librariescited by 1
  8. 2022Abstract Interpretation for Generalized Heuristic Search in Model-Based Planningcited by 0
  9. 2023The inverse problem for neural networkscited by 0
  10. 2023Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approachcited by 0
  11. 2023Compositional Inductive Invariant Based Verification of Neural Network Controlled Systemscited by 0
  12. 2024The Reachability Problem for Neural-Network Control Systemscited by 0
  13. 2024Training Verifiably Robust Agents Using Set-Based Reinforcement Learningcited by 0
  14. 2025Of Good Demons and Bad Angels: Guaranteeing Safe Control under Finite Precisioncited by 0
  15. 2025Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systemscited by 0
  16. 2026NNV3: Expanding Neural Network Verification to New Architectures and Domainscited by 0