@inproceedings{3862e1cd04e449f98ed0b9f913c96c8e,
title = "Comparative Study on Parameter Estimation Methods for Multi-fidelity Co-kriging Modeling",
abstract = "As a well-known multi-fidelity modeling technique, the co-kriging method combining the data from two or more models with different levels of fidelity to efficiently construct a metamodel has gained increasing attention to save the computational cost. Although the existing multi-fidelity co-kriging modeling approaches utilize the data from all models for response prediction, most of those methods only utilize the correlation of the data from two models with adjacent fidelities or from the same model in estimating the unknown hyper-parameters for model construction. Therefore, an enhanced co-kriging method utilizing the covariance of all observed data is developed in this paper to improve the prediction accuracy, in which the hyper-parameters are estimated altogether by maximizing a unified likelihood function. The results from the comparative studies show that the proposed co-kriging method is evidently more accurate than some existing popular approaches, demonstrating its effectiveness and relative merits.",
keywords = "Co-kriging, Covariance, Gaussian process, Hyper-parameter estimation, Multi-fidelity modeling",
author = "Yixin Liu and Shishi Chen and Fenfen Xiong",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; International Conference on Mechanical Design, ICMD 2017 ; Conference date: 13-10-2017 Through 15-10-2017",
year = "2018",
doi = "10.1007/978-981-10-6553-8_55",
language = "English",
isbn = "9789811065521",
series = "Mechanisms and Machine Science",
publisher = "Springer Netherlands",
pages = "815--836",
editor = "Changle Xiang and Jianrong Tan and Feng Gao",
booktitle = "Advances in Mechanical Design - Proceedings of the 2017 International Conference on Mechanical Design, ICMD 2017",
address = "Netherlands",
}