A variable-fidelity modeling method based on self-organizing maps spatial reduction

Variable-fidelity (VF) approximation models are wildly used to replace computational expensive simulation models in complex engineering designs. In this paper, a design space reduction variable-fidelity metamodeling (DSR-VFM) approach is proposed. In the proposed DSR-VFM, addition scaling Kriging (A...

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Bibliographic Details
Published in2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) pp. 1722 - 1726
Main Authors Ping Jiang, Leshi Shu, Xiangzheng Meng, Qi Zhou, Jiexiang Hu, Junnan Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Analytical models
Atmospheric modeling
Computational modeling
Hafnium
Kriging
Neurons
Numerical models
Self-organizing feature maps
self-organizing maps
sequential modeling
Variable-fidelity
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Summary:Variable-fidelity (VF) approximation models are wildly used to replace computational expensive simulation models in complex engineering designs. In this paper, a design space reduction variable-fidelity metamodeling (DSR-VFM) approach is proposed. In the proposed DSR-VFM, addition scaling Kriging (ASK) is chosen as the approximation model and self-organizing maps (SOM) is adopt to reduce the design space and select the key areas. Then new sample points are selected though the maximum distance method within the key areas and added to the sample set to update the approximate model. A numerical case and the modeling of the drag coefficient of an aircraft are utilized to verify the applicability of the proposed approach.
ISSN:2157-362X
DOI:10.1109/IEEM.2016.7798172