.png){kind=icon}
返回
An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2022.108858.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Global sensitivity analysis (GSA), particularly for Sobol index, is a powerful tool to quantify the variation of model response sourced from the uncertainty of input variables over the entire design space. However, GSA requires a large number of model evaluations to achieve satisfactory accuracy, which will lead to a great challenge in computational efforts when the model is expensive to be evaluated. To address this issue, an efficient method based on multi-fidelity Kriging (Cokriging) surrogate model is proposed. To this end, high dimensional model representation of Cokriging predictor is preformed to derive the analytical expressions of total and partial variances. Then, the sensitivity analysis is transformed into the computation of several one-dimensional in-tegrals, which is beneficial to reduce the computational burden. Four examples are employed to validate the performance of the proposed method. The results demonstrate that Cokriging estimator is an efficient approach to yield promising accuracy and reduce computational costs in the sensitivity analysis.
Keyword:
Global sensitivity analysis
Cokriging
Multi-fidelity surrogate model
Sobol index
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
引用论文
.png){kind=icon}
Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression基于高斯过程回归的多级多保真度稀疏多项式混沌展开
PREAI
分享收藏A generalized hierarchical co-Kriging model for multi-fidelity data fusion用于多保真度数据融合的广义分层协同Kriging模型
PREAI
分享收藏
分享收藏
查看更多内容