# Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling

## TL;DR

This paper introduces a new sensitivity analysis method for expensive black-box simulations, utilizing sequential sampling and metamodeling to reduce the number of evaluations needed for accurate results.

## Contribution

It proposes a novel sensitivity analysis algorithm that combines sequential sampling with metamodeling, optimizing evaluation efficiency for complex, costly simulations.

## Key findings

- Accurate sensitivity indices can be computed with minimal evaluations.
- Fast metamodels and specific sampling algorithms improve efficiency.
- The method effectively reduces computational cost for sensitivity analysis.

## Abstract

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.