An exact framework for uncertainty quantification in Monte Carlo simulation

20th International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Saracco, P.^1 ; Pia, M.G.^1

National Institute for Nuclear Physics (I.N.F.N.), Via Dodecaneso, 33, Genova

16146, Italy^1

关键词: Analytical relations;    Input parameter;    Particle transport;    Physics data;    Probability density function (pdf);    Statistical errors;    Uncertainty quantifications;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/513/2/022033/pdf
DOI  :  10.1088/1742-6596/513/2/022033

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In the context of Monte Carlo (MC) simulation of particle transport Uncertainty Quantification (UQ) addresses the issue of predicting non statistical errors affecting the physical results, i.e. errors deriving mainly from uncertainties in the physics data and/or in the model they embed. In the case of a single uncertainty a simple analytical relation exists among its the Probability Density Function (PDF) and the corresponding PDF for the output of the simulation: this allows a complete statistical analysis of the results of the simulation. We examine the extension of this result to the multi-variate case, when more than one of the physical input parameters are affected by uncertainties: a typical scenario is the prediction of the dependence of the simulation on input cross section tabulations.