Verification of NNs in the IMOCO4.E Project: Preliminary Results

In recent years, there has been growing interest in machine learning and neural networks within research and industrial communities. While neural networks have shown impressive capabilities across various domains, their practical applications are still limited in safety-critical contexts due to a lack of formal guarantees regarding their reliability and behavior. This paper explores the latest advancements in Satisfiability Modulo Theory (SMT) technologies for verifying neural networks with piece-wise linear and transcendent activation functions. Through experimental analysis, we evaluate these technologies using neural networks trained on a real-world predictive maintenance dataset. This research contributes to the ongoing efforts to enhance the safety and reliability of neural networks through formal verification, enabling their deployment in safety-critical domains.