<ul><li>Ensuring safety in cyber-physical systems (CPSs) is a critical challenge when system models are difficult to obtain or cannot be fully trusted.</li><li>A data-driven safety verification framework is proposed that leverages matrix zonotopes and barrier certificates to verify system safety directly from noisy data.</li><li>Instead of relying on a single unreliable model, a set of models is constructed that captures all possible system dynamics aligning with the observed data.</li><li>The model set is compactly represented using matrix zonotopes for efficient computation and propagation of uncertainty, resulting in rigorous safety guarantees without requiring an explicit system model.</li></ul>

Data-Driven Safety Verification using Barrier Certificates and Matrix Zonotopes

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