.png){kind=icon}
返回
Sequential kriging optimization using multiple-fidelity evaluations
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-005-0587-0.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
When cost per evaluation on a system of interest is high, surrogate systems can provide cheaper but lower-fidelity information. In the proposed extension of the sequential kriging optimization method, surrogate systems are exploited to reduce the total evaluation cost. The method utilizes data on all systems to build a kriging metamodel that provides a global prediction of the objective function and a measure of prediction uncertainty. The location and fidelity level of the next evaluation are selected by maximizing an augmented expected improvement function, which is connected with the evaluation costs. The proposed method was applied to test functions from the literature and a metal-forming process design problem via finite element simulations. The method manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation cost as compared to the original method.
Keyword:
multiple fidelity
surrogate systems
kriging
efficient global optimization
computer experiments
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
4.9K
被引数:
1.7W
机构
暂无机构信息
引用论文
A Comparative Analysis of Flat, Hierarchical and Location-Based Routing in Wireless Sensor Networks
PREAI
分享收藏Molecular Cloning and Characterization of the Mouse Carboxyl Ester Lipase Gene and Evidence for Expression in the Lactating Mammary Gland.png)
Genomics
GenomicsIF0
PREAI
分享收藏分享收藏Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones能源优化的移动应用程序执行: 使用云克隆驯服资源贫乏的移动设备
PREAI
分享收藏
查看更多内容