A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems
This provides a practical tool for researchers and engineers working on formal verification of AI-controlled systems, though it is incremental as it builds on existing interval analysis methods.
The authors developed a numpy toolbox for fast interval arithmetic using natural inclusion functions, enabling efficient formal verification of neural network controlled systems through composed inclusion functions.
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.