Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions
Computation
2017-05-12 v1
Abstract
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 -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.
Keywords
Cite
@article{arxiv.1705.03944,
title = {Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions},
author = {E. Burnaev and I. Panin and B. Sudret},
journal= {arXiv preprint arXiv:1705.03944},
year = {2017}
}