English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Global sensitivity analysis is used to quantify the influence of uncertaininput parameters on the response variability of a numerical model. The commonquantitative methods are applicable to computer codes with scalar inputvariables. This paper aims to illustrate different variance-based sensitivityanalysis techniques, based on the so-called Sobol indices, when some inputvariables are functional, such as stochastic processes or random spatialfields. In this work, we focus on large cpu time computer codes which need apreliminary meta-modeling step before performing the sensitivity analysis. Wepropose the use of the joint modeling approach, i.e., modeling simultaneouslythe mean and the dispersion of the code outputs using two interlinkedGeneralized Linear Models (GLM) or Generalized Additive Models (GAM). The``mean'' model allows to estimate the sensitivity indices of each scalar inputvariables, while the ``dispersion'' model allows to derive the totalsensitivity index of the functional input variables. The proposed approach iscompared to some classical SA methodologies on an analytical function. Lastly,the proposed methodology is applied to a concrete industrial computer code thatsimulates the nuclear fuel irradiation.

Global sensitivity analysis of computer models with functional inputs