JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)

Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning

Author(s): Yifan LI, Yongyong XIANG, Luojie SHI, Baisong PAN
Affiliation(s): College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Corresponding email(s): panbsz@zjut.edu.cn
Key Words: Reliability analysis; Multi-fidelity surrogate model; Active learning; Nonlinearity; Residual model

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Yifan LI, Yongyong XIANG, Luojie SHI, Baisong PAN. Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2300340

@article{title="Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning",
author="Yifan LI, Yongyong XIANG, Luojie SHI, Baisong PAN",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2300340"
}

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%T Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning
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%A Yongyong XIANG
%A Luojie SHI
%A Baisong PAN
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.A2300340"

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A1 - Yongyong XIANG
A1 - Luojie SHI
A1 - Baisong PAN
J0 - Journal of Zhejiang University Science A
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Y1 - in press
PB - Zhejiang University Press & Springer
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doi="https://doi.org/10.1631/jzus.A2300340"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: For complex engineering problems, multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources. However, most methods require nested training samples to capture the correlation between different fidelity data, which may lead to a significant increase of low-fidelity samples. In addition, it is difficult to build accurate surrogate models because current methods do not fully consider the nonlinearity between different fidelity samples. To address these problems, a novel multi-fidelity modeling method with active learning is proposed in this paper. Firstly, a nonlinear autoregressive multi-fidelity Kriging (NAMK) model is used to build a surrogate model. To avoid introducing redundant samples in the process of NAMK model updating, a collective learning function is then developed by a combination of a U-learning function, the correlation between different fidelity samples, and the sampling cost. Furthermore, a residual model is constructed to automatically generate low-fidelity samples when high-fidelity samples are selected. The efficiency and accuracy of the proposed method are demonstrated using three numerical examples and an engineering case.

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