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Data-driven Safety Verification of Stochastic Systems via Barrier Certificates
In this paper, we propose a data-driven approach to formally verify the safety of (potentially) unknown discrete-time continuous-space stochastic systems.
Data-driven verification and synthesis of stochastic systems via barrier certificates
In this work, we study verification and synthesis problems for safety specifications over unknown discrete-time stochastic systems.
Data-Driven Verification under Signal Temporal Logic Constraints
Since the dynamics are parameterized and partially unknown, we collect data from the system and employ Bayesian inference techniques to associate a confidence value to the satisfaction of the property.
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