Weighted Bayesian uncertainty quantification for the high explosive reactants using limited data - Yanjin Wang; Hao Pan; Jianwei Yin; Pei Wang; Jin Qin; Chao Li (AIP Advances)
Bayesian uncertainty analysis is a highly effective tool for estimating model uncertainty, thereby improving the prediction ability with limited data. The data quality plays a role in uncertainty analysis. This paper presents a novel approach to assess the quality of limited experiment data for high explosives. By assigning varying weights to the data based on their quality, we adopt a Bayesian statistical framework to quantify the uncertainties associated with the reactant equation of state for high explosives. The resulting quantification not only elucidates the current physical knowledge but also paves the way for more informed experimental and simulation strategies in future studies. The technique employed in this paper is not limited to high explosives and could be potentially used for uncertainty quantification of other materials.