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In this paper, we present a toolbox for interval analysis in numpy, with anapplication to formal verification of neural network controlled systems. Usingthe notion of natural inclusion functions, we systematically construct intervalbounds for a general class of mappings. The toolbox offers efficientcomputation of natural inclusion functions using compiled C code, as well as afamiliar interface in numpy with its canonical features, such as n-dimensionalarrays, matrix/vector operations, and vectorization. We then use this toolboxin formal verification of dynamical systems with neural network controllers,through the composition of their inclusion functions.

A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems