An ensemble weighted average conservative multi-fidelity surrogate modeling method for engineering optimization-Reference-Cited by-同舟云学术

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An ensemble weighted average conservative multi-fidelity surrogate modeling method for engineering optimization

Published:2020-11-09 Issue: Volume: Page:
ISSN:0177-0667
Container-title:Engineering with Computers
language:en
Short-container-title:Engineering with Computers

Author:

Hu Jiexiang,Peng Yutong,Lin Quan,Liu Huaping,Zhou Qi

Funder

National Natural Science Foundation of China

National Defense Innovation Program

China Scholarship Council with a Scholarship

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modelling and Simulation,Software

Link

http://link.springer.com/content/pdf/10.1007/s00366-020-01203-8.pdf

Reference46 articles.

1. Zhou Q, Wu J, Xue T, Jin P (2019) A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems. Eng Comput. https://doi.org/10.1007/s00366-019-00844-8

2. Zhou Q, Wu Y, Guo Z, Hu J, Jin P (2020) A generalized hierarchical co-Kriging model for multi-fidelity data fusion. Struct Multidiscipl Optim. https://doi.org/10.1007/s00158-020-02583-7

3. Belyaev M, Burnaev E, Kapushev E, Panov M, Prikhodko P, Vetrov D, Yarotsky D (2016) Gtapprox: Surrogate modeling for industrial design. Adv Eng Softw 102:29–39

4. Dong H, Li C, Song B, Wang P (2018) Multi-surrogate-based Differential Evolution with multi-start exploration (MDEME) for computationally expensive optimization. Adv Eng Softw 123:62–76

5. Hu J, Zhou Q, McKeand A, Xie T, Choi S-K (2020) A model validation framework based on parameter calibration under aleatory and epistemic uncertainty. Struct Multidiscipl Optim. https://doi.org/10.1007/s00158-020-02715-z

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