Competitive comparison of optimal designs of experiments for sampling-based sensitivity analysis

A widely used strategy to explore the sensitivity of the model to its inputs is based on a finite set of simulations. These are usually performed for a chosen set of points in a parameter space. An estimate of the sensitivity can be then obtained by computing correlations between the model inputs and outputs. The accuracy of the sensitivity prediction depends on a quality of the points distribution in the parameter space, so-called the design of experiments. The aim of the presented paper is to review and compare available criteria determining an optimal design of experiments for sampling-based sensitivity analysis.