Tracking and Detecting Systematic Errors in Digital Twins

Digital Twins (DTs) have immense promise for exploiting the power of computer simulation to control large-scale real-world systems. The key idea is to evaluate or optimize decisions using the DT, and then implement them in the real-world system. Even with best practices, the DT and the real-world system may become misaligned over time. In this paper we provide a statistical method to detect such misalignment even though both the simulation and the real-world system are inherently stochastic. An empirical evaluation and a realistic illustration are provided.