A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems
Systems and Control
2023-06-28 v1 Machine Learning
Systems and Control
Optimization and Control
Abstract
In this paper, we present a toolbox for interval analysis in numpy, with an application to formal verification of neural network controlled systems. Using the notion of natural inclusion functions, we systematically construct interval bounds for a general class of mappings. The toolbox offers efficient computation of natural inclusion functions using compiled C code, as well as a familiar interface in numpy with its canonical features, such as n-dimensional arrays, matrix/vector operations, and vectorization. We then use this toolbox in formal verification of dynamical systems with neural network controllers, through the composition of their inclusion functions.
Keywords
Cite
@article{arxiv.2306.15340,
title = {A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems},
author = {Akash Harapanahalli and Saber Jafarpour and Samuel Coogan},
journal= {arXiv preprint arXiv:2306.15340},
year = {2023}
}