Optimal Uncertainty Quantification of a risk measurement from a\n thermal-hydraulic code using Canonical Moments

2019/01/22 by Jérôme Stenger, Stenger, Jerome, Fabrice Gamboa +5 · 1 citation
Decision Sciences · Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Methodology (stat.ME) #Nuclear Engineering Thermal-Hydraulics #Probabilistic and Robust Engineering Design #Probability (math.PR)

paper · pdf · doi:10.48550/arxiv.1901.07903

openalex publication_date 2019/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study an industrial computer code related to nuclear safety. A major topic\nof interest is to assess the uncertainties tainting the results of a computer\nsimulation. In this work we gain robustness on the quantification of a risk\nmeasurement by accounting for all sources of uncertainties tainting the inputs\nof a computer code. To that extent, we evaluate the maximum quantile over a\nclass of distributions defined only by constraints on their moments. Two\noptions are available when dealing with such complex optimization problems: one\ncan either optimize under constraints; or preferably, one should reformulate\nthe objective function. We identify a well suited parameterization to compute\nthe optimal quantile based on the theory of canonical moments. It allows an\neffective, free of constraints, optimization.\n

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