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首页> 外文期刊>Mechanical systems and signal processing >An enhanced Kriging surrogate modeling technique for high-dimensional problems
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An enhanced Kriging surrogate modeling technique for high-dimensional problems

机译：用于高维问题的增强型Kriging代理建模技术

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摘要

Surrogate modeling techniques are widely used to simulate the behavior of manufactured and engineering systems. The construction of such surrogate models may become intractable in cases when input spaces have high dimensions, because the large number of model responses is typically required to estimate model parameters. In this paper, we proposed a new Kriging modeling technique combined with dimension reduction method to address the issue. In the proposed method, the sliced inverse regression technique is utilized to achieve a dimension reduction by constructing a new projection vector which reduces the dimension of the original input vector without losing the essential information of the model response quantify of interest. In the dimension reduction subspace, a new correlation function of Kriging is constructed by means of the tensor product of several correlation functions with respect to each projection direction. The proposed method is especially promising for high-dimensional problems. In examples including finite element model (FEM) pertinent to low cycle fatigue life (LCF) of a aero-engine compressor disc, the enhanced Kriging is found to outperform several well-established surrogate models when small sample sizes are used.
机译：代理建模技术被广泛用于模拟制造和工程系统的行为。在输入空间具有高维的情况下，此类替代模型的构造可能变得棘手，因为通常需要大量的模型响应来估计模型参数。在本文中，我们提出了一种新的Kriging建模技术并结合了降维方法来解决该问题。在提出的方法中，通过构造一个新的投影向量来利用切分逆回归技术来实现降维，该投影向量可以减小原始输入向量的维数，而不会丢失模型响应量化的基本信息。在降维子空间中，借助于相对于每个投影方向的多个相关函数的张量积，构造了克里金的新相关函数。所提出的方法对于高维问题特别有希望。在包括与航空发动机压气机盘低周疲劳寿命（LCF）相关的有限元模型（FEM）的示例中，发现使用较小的样本量时，增强的Kriging优于某些公认的替代模型。

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