  Simulations are becoming ever more common as a tool for designing complex
products. Sensitivity analysis techniques can be applied to these simulations
to gain insight, or to reduce the complexity of the problem at hand. However,
these simulators are often expensive to evaluate and sensitivity analysis
typically requires a large amount of evaluations. Metamodeling has been
successfully applied in the past to reduce the amount of required evaluations
for design tasks such as optimization and design space exploration. In this
paper, we propose a novel sensitivity analysis algorithm for variance and
derivative based indices using sequential sampling and metamodeling. Several
stopping criteria are proposed and investigated to keep the total number of
evaluations minimal. The results show that both variance and derivative based
techniques can be accurately computed with a minimal amount of evaluations
using fast metamodels and FLOLA-Voronoi or density sequential sampling
algorithms.
