  In this work, we propose a novel sampling method for Design of Experiments.
This method allows to sample such input values of the parameters of a
computational model for which the constructed surrogate model will have the
least possible approximation error. High efficiency of the proposed method is
demonstrated by its comparison with other sampling techniques (LHS, Sobol'
sequence sampling, and Maxvol sampling) on the problem of least-squares
polynomial approximation. Also, numerical experiments for the Lebesgue constant
growth for the points sampled by the proposed method are carried out.
