Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The existence of a barrier certificate formally verifies the safety of the dynamical system. Recent approaches synthesize barrier certificates by iteratively training a neural network. In each iteration, the candidate is formally verified - if successful, the barrier certificate is found. Instead, we propose a set-based training approach that tightly integrates verification into training via a set-based loss function that soundly encodes all barrier certificate properties. A loss of zero formally proves the validity of the barrier certificate, coll...
| Authors | Miriam Kranzlmüller, Lukas Koller, Tobias Ladner et al. |
|---|---|
| Primary category | eess.SY |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.