OPTIMAL UNCERTAINTY QUANTIFICATION OF A RISK MEASUREMENT FROM A THERMAL-HYDRAULIC CODE USING CANONICAL MOMENTS

DOI10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2020030800zbMATH Open1498.76074arXiv1901.07903OpenAlexW3001995608WikidataQ126303277 ScholiaQ126303277MaRDI QIDQ5052386

Jérôme Stenger, Merlin Keller, F. Gamboa, B. Iooss

Publication date: 24 November 2022

Published in: International Journal for Uncertainty Quantification (Search for Journal in Brave)

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

Full work available at URL: https://arxiv.org/abs/1901.07903

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Stochastic analysis applied to problems in fluid mechanics (76M35)

Related Items (2)

Optimization of Quasi-convex Function over Product Measure Sets ⋮ An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes

This page was built for publication: OPTIMAL UNCERTAINTY QUANTIFICATION OF A RISK MEASUREMENT FROM A THERMAL-HYDRAULIC CODE USING CANONICAL MOMENTS

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q5052386)