Quadratic Characterizations for Reachability Analysis of Neural Networks
The framework creates verified quadratic constraints to soundly approximate nonlinear relationships in 2D space. It generates candidate inequalities from data using quadratic programs and then rigorously verifies them globally using sum-of-squares certificates. This allows for safe overapproximations of scalar relations, which are useful for analyzing neural network reachability and safety.