Continuous-time Data-driven Barrier Certificate Synthesis

Understand

We consider the problem of verifying safety for continuous-time dynamical systems.

• Developing upon recent advancements in data-driven verification, we use only a finite number of sampled trajectories to learn a barrier certificate, namely a function which verifies safety.
• We train a safety-informed neural network to act as this certificate, with an appropriately designed loss function to encompass the safety conditions.
• In addition, we provide probabilistic generalisation guarantees from discrete samples of continuous trajectories, to unseen continuous ones.