  Global sensitivity analysis aims at quantifying respective effects of input
random variables (or combinations thereof) onto variance of a physical or
mathematical model response. Among the abundant literature on sensitivity
measures, Sobol' indices have received much attention since they provide
accurate information for most of models. We consider a problem of experimental
design points selection for Sobol' indices estimation. Based on the concept of
$D$-optimality, we propose a method for constructing an adaptive design of
experiments, effective for calculation of Sobol' indices based on Polynomial
Chaos Expansions. We provide a set of applications that demonstrate the
efficiency of the proposed approach.
