Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions

2017/05/10 by Evgeny Burnaev, Burnaev, E., Ivan Panin +3
Agricultural and Biological Sciences · Decision Sciences · Engineering · #Computation (stat.CO) #FOS: Computer and information sciences #Probabilistic and Robust Engineering Design #Rice Cultivation and Yield Improvement #Structural Response to Dynamic Loads

paper · pdf · doi:10.48550/arxiv.1705.03944

openalex publication_date 2017/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 D-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.

Citations

• A Note on the Delta Method
• THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED

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