A conservative multi-fidelity surrogate model-based robust optimization method for simulation-based optimization-Reference-Cited by-同舟云学术

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A conservative multi-fidelity surrogate model-based robust optimization method for simulation-based optimization

Published:2021-07-29 Issue:4 Volume:64 Page:2525-2551
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Hu Jiexiang,Zhang Lili,Lin Quan,Cheng Meng,Zhou Qi,Liu Huaping

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

National Defense Innovation Program

Research Funds of the Maritime Defense Technologies Innovation

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/s00158-021-03007-w.pdf

Reference48 articles.

1. Acar P, Sundararaghavan V (2019) Stochastic design optimization of microstructural features using linear programming for robust design. AIAA J 57:448–455

2. Alinejad F, Botto D (2019) Innovative adaptive penalty in surrogate-assisted robust optimization of blade attachments. Acta Mech 230:2735–2750

3. Chatterjee T, Chakraborty S, Chowdhury R (2019) A critical review of surrogate assisted robust design optimization. Archives of Computational Methods in Engineering 26:245–274

4. Chunna L, Hai F, Chunlin G (2020) Development of an efficient global optimization method based on adaptive infilling for structure optimization. Struct Multidisc Optim 62, 3383–3412. https://doi.org/10.1007/s00158-020-02716-y

5. Dasari SK, Cheddad A, Andersson P (2020) Predictive modelling to support sensitivity analysis for robust design in aerospace engineering. Struct Multidisc Optim 61, 2177–2192. https://doi.org/10.1007/s00158-019-02467-5

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