The integration of AI into Cyber-Physical Systems (CPS) has enhanced their functionality but introduced challenges for traditional verification methods. Temporal logic falsification techniques, which are designed for deterministic models, struggle with the complexity of AI-driven systems. To address these challenges, this Ph.D. dissertation focuses on two primary directions: (i) conduct an empirical analysis to categorize CPS models, identify verification challenges specific to AI systems, and (ii) propose a novel falsification method that combines stochastic optimization and reinforcement learning to improve fault detection in AI-enabled CPS. This research is expected to contribute significantly to the CPS verification domain by improving the accuracy and efficiency of the verification process and providing a public data set to support further research.

In the world of Cyber-Physical Systems (CPS), a captivating real-time fusion occurs where digital technology meets the physical world. This synergy has been significantly transformed by the integration of artificial intelligence (AI), a move that dramatically enhances system adaptability and introduces a layer of complexity that impacts CPS control optimization and reliability. Despite advancements in AI integration, a significant gap remains in understanding how this shift affects CPS architecture, operational complexity, and verification practices. The extended abstract addresses this gap by investigating architectural distinctions between AI-driven and traditional control models designed in Simulink and their respective implications for system verification.