On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning

Zuhal Lavi Rizki; Faza Ghifari Adam; Palar Pramudita Satria; Liem Rhea Patricia

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On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning

机译：基于Kriging的可靠性分析与主动学习的局部最小二乘规模减少

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Kriging with active learning has been widely employed to calculate the failure probability of a problem with random inputs. Training a Kriging model for a high-dimensional problem is computationally expensive. This reduces the efficiency of active learning strategies since the training cost becomes comparable to that of the function evaluation itself. Kriging with partial least squares (KPLS) has the advantage of a fast training time but its efficacy on high-dimensional reliability analysis has not yet been properly investigated. In this paper, we assess the potential benefits of KPLS for solving high-dimensional reliability analysis problems. This research aims to identify the potential advantages of KPLS and characterize the problem domain where KPLS can be most efficient and accurate in estimating the failure probability. Tests on a set of benchmark problems with various dimensionalities reveal that KPLS with four principal components significantly reduces the CPU time compared to the ordinary Kriging while still achieving accurate failure probability. In some problems, it is also observed that KPLS with four principal components reduces the number of function evaluations, which will be beneficial for problems with expensive function evaluations. Using too few principal components, however, does not show any evident improvements over ordinary Kriging.

机译：具有主动学习的Kriging已被广泛用于计算随机输入问题的故障概率。培训高维问题的Kriging模型是计算昂贵的。这降低了积极学习策略的效率，因为培训成本与函数评估本身的竞争性相当。克里格与部分最小二乘（KPLS）具有快速训练时间的优点，但其对高维可靠性分析的功效尚未得到适当的研究。在本文中，我们评估了KPLS来解决高维可靠性分析问题的潜在益处。该研究旨在识别KPLS的潜在优势，并表征问题域，其中KPLS可以最有效，估计失效概率。在各种维度的一组基准问题上测试显示，与普通克里格相比，具有四个主要成分的KPLS与普通克里格相比，与普通的克里格相比显着降低了CPU时间，同时仍然实现了准确的失效概率。在一些问题中，还观察到具有四个主成分的KPLS减少了函数评估的数量，这将有利于昂贵的功能评估问题。然而，使用太少的主要成分并没有显示出对普通克里格的任何明显的改进。

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来源
《Reliability Engineering & System Safety》 |2021年第11期|107848.1-107848.13|共13页
作者
Zuhal Lavi Rizki; Faza Ghifari Adam; Palar Pramudita Satria; Liem Rhea Patricia;
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作者单位

Inst Teknol Bandung Fac Mech & Aerosp Engn Java Indonesia;

Inst Teknol Bandung Fac Mech & Aerosp Engn Java Indonesia;

Inst Teknol Bandung Fac Mech & Aerosp Engn Java Indonesia;

Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Hong Kong Peoples R China;

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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
Reliability analysis; Kriging; Active learning; Partial least squares; Dimensionality reduction;

机译：可靠性分析;Kriging;主动学习;部分最小二乘;维数减少;

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On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning

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