Uncertainty evaluation using virtual experiments: bridging JCGM 101 and a Bayesian framework

Abstract The applications of virtual metrology and the concept of using virtual experiments (VEs) or digital twins in measurement tasks have gained significant interest in recent years. When real measurements are costly or scarce, a VE can replicate complex mathematical and physical models. As a digital representation of a measurement process, a VE can, among other use-cases, be used in an uncertainty evaluation. Here, simulated and real observations can be combined without explicit knowledge of a model for the measurand – a usual requirement for uncertainty evaluation according to the GUM and its supplement JCGM 101. Including the variability of repeated measurements in a VE can be a challenging task. However, metrologists are aware of the potential impact of repeatability conditions and usually have significant knowledge about the precision of their measurement instrument. In this work, we develop a Monte Carlo sampling approach and software to perform an uncertainty evaluation that includes such prior knowledge about the precision of the measurement device, requiring only evaluations of the VE. Using an example of a calibrated measurement application, we demonstrate that there is a notable benefit to using available prior knowledge during an uncertainty evaluation, particularly in the case of limited observations.