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Modified screening-based Kriging method with cross validation and application to engineering design

机译：基于交叉筛选的改进的基于筛选的克里格法及其在工程设计中的应用

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摘要

In this paper, a basis screening Kriging method using cross validation error is proposed to alleviate computational burden of the dynamic Kriging while maintaining its accuracy. Metamodeling is widely used for design optimization of complex engineering applications where considerable computation time is required. The Kriging method is one of popular metamodeling methods due to its accuracy and efficiency. There have been many attempts to improve accuracy of the Kriging method, and the dynamic Kriging method using cross-validation error, which selects adequate basis functions to best describe the mean structure of a response using a genetic algorithm, achieves outstanding performance in terms of accuracy. However, despite its accuracy, the dynamic Kriging requires very large amounts of computation because of the genetic algorithm and no limitation for order of basis functions. In the proposed method, a basis function set is determined by screening each basis function instead of using the genetic algorithm, which has advantages in computation for high dimensional metamodels or repeated metamodel generation. Numerical studies with four mathematical examples and two engineering applications verify that the proposed basis screening Kriging significantly reduces computation time with similar accuracy as the dynamic Kriging. (C) 2019 Elsevier Inc. All rights reserved.
机译：本文提出了一种使用交叉验证误差的基础筛选克里金法，以减轻动态克里金算法的计算负担，同时保持其准确性。元模型被广泛用于需要大量计算时间的复杂工程应用的设计优化。克里格方法由于其准确性和效率而成为流行的元建模方法之一。已经进行了许多尝试来改进Kriging方法的准确性，并且使用交叉验证误差的动态Kriging方法选择了适当的基函数以使用遗传算法来最佳地描述响应的平均结构，从而在准确性方面取得了出色的性能。 。然而，尽管具有精度，但是由于遗传算法的存在，动态克里金法仍然需要非常大量的计算，并且对基函数的阶数没有限制。在提出的方法中，通过筛选每个基本函数而不是使用遗传算法来确定基本函数集，这在计算高维元模型或重复生成元模型方面具有优势。带有四个数学示例和两个工程应用的数值研究证明，所提出的基础筛选Kriging以与动态Kriging相似的精度显着减少了计算时间。 （C）2019 Elsevier Inc.保留所有权利。

著录项

  • 来源
    《Applied Mathematical Modelling》 |2019年第6期|626-642|共17页
  • 作者

    Kang Kyeonghwan; Qin Caiyan; Lee Bongjae; Lee Ikjin;

  • 作者单位

    Korea Adv Inst Sci & Technol, Dept Mech Engn, Daejeon, South Korea;

    Korea Adv Inst Sci & Technol, Dept Mech Engn, Daejeon, South Korea;

    Korea Adv Inst Sci & Technol, Dept Mech Engn, Daejeon, South Korea;

    Korea Adv Inst Sci & Technol, Dept Mech Engn, Daejeon, South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Metamodel; Surrogate model; Kriging; Cross validation;

    机译：元模型代理模型克里格交叉验证;

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