Continuous-time Data-driven Barrier Certificate Synthesis

Luke Rickard, Alessandro Abate, Kostas Margellos

Abstract: 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. Numerical investigations demonstrate the efficacy of our approach and contrast it with related results in the literature.