Learning Models of Cyber-Physical Systems with Discrete and Continuous Behaviour for Digital Twin Synthesis

Digital twins are used to simulate (cyber-physical) systems and offer great benefits for testing and verification. The importance of quickly and efficiently constructing digital twins increases with the appearance of devices of greater complexity. Furthermore, the more (varied) behaviour the digital twin captures of the simulated device the more use cases it can be used for. In the presented thesis we investigate methods from automata learning and machine learning to automatically synthesise digital twins from cyber-physical systems, capturing both discrete and continuous behaviour. Our aim hereby is to combine methods from both fields and utilize their respective strengths to build better digital twins from cyber-physical systems in practice. We already developed an algorithm that learns discrete behavioural models even in the presence of noisy data.