Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

摘要: Abstract Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume the variable is scalar. These are applied on each leading a large number sensitivity indices high redundancy making interpretation results difficult. Two have been proposed for GSA case output: decomposition approach [9] covariance [14] but they computationally intensive most practical problems. In this paper, Polynomial Chaos Expansion (PCE) an efficient with output. The indicate PCE allows estimation matrix coefficients defined by Campbell et al. , development analytical expressions Gamboa .