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

• Published in: 2016 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
• Language: English
• 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
• DOI: 10.1109/IEEM.2016.7798172

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.