Competitive Comparison of Optimal Designs of Experiments for Sampling-based Sensitivity Analysis
For practitioners using sensitivity analysis, this provides a comparative review of design quality criteria, but the contribution is incremental as it does not introduce new methods or benchmarks.
The paper reviews and compares criteria for evaluating designs of experiments used in sampling-based sensitivity analysis, aiming to identify which criteria best predict accuracy. No concrete numerical results are provided.
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.