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Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning
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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, 1998, -1(-1): .
@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",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2300340"
}
%0 Journal Article
%T Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning
%A Yifan LI
%A Yongyong XIANG
%A Luojie SHI
%A Baisong PAN
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2300340
TY - JOUR
T1 - Efficient reliability analysis via a nonlinear autoregressive multi-fidelity surrogate model and active learning
A1 - Yifan LI
A1 - Yongyong XIANG
A1 - Luojie SHI
A1 - Baisong PAN
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2300340
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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