An active learning approach for multi-objective optimization and its application to the crashworthiness design of thin-walled structures-Reference-Cited by-同舟云学术

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An active learning approach for multi-objective optimization and its application to the crashworthiness design of thin-walled structures

Published:2023-08-05 Issue: Volume: Page:
ISSN:1389-4420
Container-title:Optimization and Engineering
language:en
Short-container-title:Optim Eng

Author:

Gao Dawei,Li Qiang,Liu Zhe,Chang Gaoshuang

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Control and Optimization,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s11081-023-09821-z.pdf

Reference51 articles.

1. Acar E, Rais-Rohani M (2009) Ensemble of metamodels with optimized weight factors. Struct Multidiscip Optim 37(3):279–294

2. Akhtar T, Shoemaker CA (2016) Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. J Global Optim 64(1):17–32

3. Baykasoğlu A, Baykasoğlu C, Cetin E (2020) Multi-objective crashworthiness optimization of lattice structure filled thin-walled tubes. Thin-Walled Struct 149:106630

4. Chen WC, Hsu YY, Hsieh LF, Tai PH (2010) A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN. Expert Syst Appl 37(1):716–726

5. Chen CT, Gu GX (2020) Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci 7(5):1902607

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