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Many mathematical models involve input parameters, which are not preciselyknown. Global sensitivity analysis aims to identify the parameters whoseuncertainty has the largest impact on the variability of a quantity of interest(output of the model). One of the statistical tools used to quantify theinfluence of each input variable on the output is the Sobol sensitivity index,which can be estimated using a large sample of evaluations of the output. Wepropose a variance reduction technique, based on the availability of a fastapproximation of the output, which can enable significant computational savingswhen the output is costly to evaluate.

Multifidelity variance reduction for pick-freeze Sobol index estimation