Uncertainty quantification in numerical simulation

Abstract

Uncertainty Quantification (UQ) is expected to be the key to evaluating the realistic reproducibility of models. However, the computational costs associated with considering a large number of variability factors and establishing the accuracy and validity of simulation models can be challenging. This paper introduces the integration of deterministic numerical simulations with measured data, utilizing machine learning and the uncertainty quantification tool SmartUQ. This approach makes it possible to quantitatively evaluate various uncertainty factors and easily introduce probabilistic analysis to understand complex real-world phenomena.