  Many mathematical models involve input parameters, which are not precisely
known. Global sensitivity analysis aims to identify the parameters whose
uncertainty has the largest impact on the variability of a quantity of interest
(output of the model). One of the statistical tools used to quantify the
influence 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. We
propose a variance reduction technique, based on the availability of a fast
approximation of the output, which can enable significant computational savings
when the output is costly to evaluate.
