Competitive Comparison of Optimal Designs of Experiments for Sampling-based Sensitivity Analysis

TL;DR

This paper reviews and compares various optimal experimental designs for sampling-based sensitivity analysis, crucial for understanding complex models efficiently despite high computational costs.

Contribution

It provides a comprehensive comparison of criteria for selecting experimental designs tailored for sensitivity analysis, highlighting their relative effectiveness.

Findings

Certain design criteria outperform others in accuracy

Optimal designs reduce computational effort in sensitivity analysis

Guidelines for choosing experimental designs are proposed

Abstract

Nowadays, the numerical models of real-world structures are more precise, more complex and, of course, more time-consuming. Despite the growth of a computational effort, the exploration of model behaviour remains a complex task. The sensitivity analysis is a basic tool for investigating the sensitivity of the model to its inputs. One widely used strategy to assess the sensitivity is based on a finite set of simulations for a given sets of input parameters, i.e. points in the design space. An estimate of the sensitivity can be then obtained by computing correlations between the input parameters and the chosen response of the model. The accuracy of the sensitivity prediction depends on the choice of design points called the design of experiments. The aim of the presented paper is to review and compare available criteria determining the quality of the design of experiments suitable for sampling-based sensitivity analysis.