自适应降维切比雪夫元模型

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software Pub Date : 2024-07-10 DOI:10.1016/j.advengsoft.2024.103720

Yichen Zhou, Feng Li, Hongfeng Li, Shijun Qu

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引用次数: 0

摘要

为了平衡降维切比雪夫元模型的精度和效率，我们提出了一种自适应降维切比雪夫元模型（ADC）。通过降维方法和切比雪夫元模型，构建了单变量降维切比雪夫元模型（UDC）。在 UDC 的基础上，使用自适应选择方法选出对元模型影响较大的二变量项，并与 UDC 结合，构建 ADC。由于增加了更多的计算样本点，ADC 比 UDC 具有更高的精度。与二维降维切比雪夫元模型相比，ADC 需要的样本点更少，效率更高。数值示例结果表明，与其他常用元模型相比，ADC 具有更高的精度，更适合逼近高维复杂模型。

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An adaptive dimension-reduction Chebyshev metamodel

An adaptive dimension-reduction Chebyshev metamodel (ADC) is proposed to balance the accuracy and efficiency of dimension-reduction Chebyshev metamodels. A univariate dimension-reduction Chebyshev metamodel (UDC) is constructed by the dimension-reduction method and the Chebyshev metamodel. Based on the UDC, the bivariate terms largely impacting the metamodel are selected using an adaptive selection method, and are combined with the UDC to construct the ADC. The ADC has higher accuracy than the UDC because more calculated sample points are added. Compared with the bivariate dimension-reduction Chebyshev metamodel, the ADC needs fewer sample points and has higher efficiency. The result of numerical examples illustrate that ADC has higher accuracy compared with other commonly-used metamodels and is more suitable for approximating high-dimensional complex models.

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来源期刊

Advances in Engineering Software 工程技术-计算机：跨学科应用

CiteScore

7.70

自引率

4.20%

发文量

169

审稿时长

37 days

期刊介绍： The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving. The scope of the journal includes: • Innovative computational strategies and numerical algorithms for large-scale engineering problems • Analysis and simulation techniques and systems • Model and mesh generation • Control of the accuracy, stability and efficiency of computational process • Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing) • Advanced visualization techniques, virtual environments and prototyping • Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations • Application of object-oriented technology to engineering problems • Intelligent human computer interfaces • Design automation, multidisciplinary design and optimization • CAD, CAE and integrated process and product development systems • Quality and reliability.