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A Physics-Informed Scenario Approach with Data Mitigation for Safety Verification of Nonlinear Systems

This paper develops a physics-informed scenario approach for safety verification of nonlinear systems using barrier certificates (BCs) to ensure that system trajectories remain within safe regions over an infinite time horizon. Designing BCs often relies on an accurate dynamics model; however, such models are often imprecise due to the model complexity involved, particularly when dealing with highly nonlinear systems. In such cases, while scenario approaches effectively address the safety problem using collected data to construct a guaranteed BC for the dynamical system, they often require substantial amounts of data-sometimes millions of samples-due to exponential sample complexity. To address this, we propose a physics-informed scenario approach that selects data samples such that the outputs of the physics-based model and the observed data are sufficiently close (within a specified threshold). This approach guides the scenario optimization process to eliminate redundant samples and significantly reduce the required dataset size. We demonstrate the capability of our approach in mitigating the amount of data required for scenario optimizations with both deterministic (i.e., confidence 1) and probabilistic (i.e., confidence between 0 and 1) guarantees. We validate our physics-informed scenario approach through two physical case studies, showcasing its practical application in reducing the required data.

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