Article

Verifiable Safe Synthesis Method for Stochastic Neural Control Barrier Functions (SNCBFs)

This study proposes a verifiable safe synthesis method for stochastic neural control barrier functions (SNCBFs), providing a verification-free synthesis framework and a verification-in-the-loop synthesis framework for the smooth activation function and ReLU cases, respectively. The method was validated on three cases: the inverted pendulum, Darboux, and unicycle models.

Part of the event: Proposes a verifiable safe synthesis framework for stochastic neural control barrier functions

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