Verisig 2.0: verification of neural network controllers using Taylor model preconditioning

DOI10.1007/978-3-030-81685-8_11zbMath1493.68211OpenAlexW3184441520MaRDI QIDQ832173

James Weimer, Rajeev Alur, Taylor Carpenter, Radoslav Ivanov, Insup Lee, George J. Pappas

Publication date: 25 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-81685-8_11

Mathematics Subject Classification ID

Nonlinear ordinary differential equations and systems (34A34) General methods in interval analysis (65G40) Specification and verification (program logics, model checking, etc.) (68Q60) Numerical methods for initial value problems involving ordinary differential equations (65L05) Control/observation systems governed by ordinary differential equations (93C15)

Related Items (4)

Verisig ⋮ T4V: exploring neural network architectures that improve the scalability of neural network verification ⋮ Automatic Abstraction Refinement in Neural Network Verification using Sensitivity Analysis ⋮ Reachability analysis of a general class of neural ordinary differential equations

Uses Software

• S-TaLiRo
• SpaceEx
• Flow*
• Breach
• Reluplex
• AI2
• ReachNN
• Scenic

Cites Work

• Unnamed Item
• The algorithmic analysis of hybrid systems
• Safety verification of deep neural networks
• Reluplex: an efficient SMT solver for verifying deep neural networks
• Verification of deep convolutional neural networks using ImageStars
• S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems
• Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
• Case study
• Formal verification of neural network controlled autonomous systems
• Reachability analysis for neural feedback systems using regressive polynomial rule inference
• Verisig
• Gray-box adversarial testing for control systems with machine learning components
• On Taylor Model Based Integration of ODEs

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