Synthesizing Formally Verified Stochastic Neural Control Barrier Functions for Safety-Critical Control

This paper presents an algorithm to synthesize a formally verified continuous-time neural Control Barrier Function (CBF) in stochastic environments in a single step. The proposed training process ensures efficacy across the entire state space with only a finite number of data points by constructing a sample-based learning framework for Stochastic Neural CBFs (SNCBFs).