Gaussian process regression based on deep neural network for reliability analysis in high dimensions-Reference-Cited by-同舟云学术

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Gaussian process regression based on deep neural network for reliability analysis in high dimensions

Published:2023-05-23 Issue:6 Volume:66 Page:
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Zhou Tong,Peng Yongbo^ORCID

Funder

Committee of Science and Technology of Shanghai China

Ministry of Science and Technology of China

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Graphics and Computer-Aided Design,Computer Science Applications,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s00158-023-03582-0.pdf

Reference55 articles.

1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. https://doi.org/10.1109/TAC.1974.1100705

2. Amalnerkar E, Lee TH, Lim W (2020) Reliability analysis using bootstrap information criterion for small sample size response functions. Struct Mutltidisc Optim 62:2901–2913. https://doi.org/10.1007/s00158-020-02724-y

3. Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79. https://doi.org/10.1214/09-SS054

4. Au SK, Beck JL (2001) Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 16(4):263–277. https://doi.org/10.1016/S0266-8920(01)00019-4

5. Bichon BJ, Eldred MS, Swiler LP, Mahadevan S, McFarland JM (2008) Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J 46(10):2459–2468. https://doi.org/10.2514/1.34321

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AK-Gibbs: An active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities;Computer Methods in Applied Mechanics and Engineering;2024-06

2. Bayesian reinforcement learning reliability analysis;Computer Methods in Applied Mechanics and Engineering;2024-05

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