Volume 8, No. 4, July 2019

General Information

  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian R. McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
  • Abstracting/Indexing: Scopus (since 2016), CNKI, Google Scholar, Crossref, etc.
  • E-mail questions to IJMERR Editorial Office.

Submissions

Please send your full manuscript to:

ijmerr@vip.163.com


Useful Documents

Paper Template

Copyright Transfer Agreement

Application For Reviewers

Contact us

International Journal of Mechanical Engineering and Robotics Research
E-mail: ijmerr@vip.163.com

An Improved Co-Kriging Multi-fidelity Surrogate Modeling Method for Non-nested Sampling Data

Xiongfeng Ruan 1, Ping Jiang 1, Qi Zhou 2, and Yang Yang 3
1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, China
2. School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan, China
3. College of Engineering, Huazhong Agricultural University, Wuhan, China

Abstract—The multi-fidelity surrogate model, which can effectively balance the prediction accuracy and the modeling cost shows enormous potential in the design and optimization of mechanical products. Among them, the Co-Kriging multi-fidelity surrogate model based on Bayesian theory can provide the prediction error at the non-test points, which makes it especially attractive in the field of design optimization under uncertainty. However, in the Co-Kriging modeling process, high-fidelity (HF) and low-fidelity (LF) sampling points must be nested to satisfy the Markov property. If the Co-Kriging coefficients are obtained based on the full correlation, the modeling process will be complicated and result in low modeling efficiency. Therefore, this paper proposes an improved Co-Kriging multi-fidelity surrogate modeling method for non-nested sampling data. The proposed approach makes use of the characteristics of the stochastic kriging model to take the error of the LF surrogate model into consideration. Two independent processes are used to get the hyper-parameters of the LF surrogate model and the discrepancy model, respectively. The prediction accuracy and robustness of the proposed method are compared to the existing typical multi-fidelity surrogate modeling method on a standard numerical test example and an engineering example. The comparison results indicate that the proposed approach possesses not only excellent prediction accuracy but also outstanding robustness. 

Index Terms—multi-fidelity surrogate model, uncertainty, Non-nested sampling data, Co-Kriging model

Cite: Xiongfeng Ruan, Ping Jiang, Qi Zhou, and Yang Yang, "An Improved Co-Kriging Multi-fidelity Surrogate Modeling Method for Non-nested Sampling Data" International Journal of Mechanical Engineering and Robotics Research, Vol. 8, No. 4, pp. 559-564, July 2019. DOI: 10.18178/ijmerr.8.4.559-564