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

## TL;DR

This paper introduces an adaptive experimental design method based on polynomial chaos expansions and D-optimality for efficient estimation of Sobol' sensitivity indices in global sensitivity analysis.

## Contribution

It proposes a novel adaptive design approach for selecting experimental points to accurately estimate Sobol' indices using polynomial chaos expansions.

## Key findings

- The method improves efficiency in sensitivity analysis.
- Applications demonstrate the effectiveness of the proposed approach.
- The approach reduces computational costs for Sobol' indices estimation.

## 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.