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A multi-surrogate approximation method for metamodeling

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Abstract

Metamodeling methods have been widely used in engineering applications to create surrogate models for complex systems. In the past, the input–output relationship of the complex system is usually approximated globally using only a single metamodel. In this research, a new metamodeling method, namely multi-surrogate approximation (MSA) metamodeling method, is developed using multiple metamodels when the sample data collected from different regions of the design space are of different characteristics. In this method, sample data are first classified into clusters based on their similarities in the design space, and a local metamodel is identified for each cluster of the sample data. A global metamodel is then built using these local metamodels considering the contributions of these local metamodels in different regions of the design space. Compared with the traditional approach of global metamodeling using only a single metamodel, this MSA metamodeling method can improve the modeling accuracy considerably. Applications of this metamodeling method have also been demonstrated in this research.

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Acknowledgments

This research is supported by Natural Science and Engineering Research Council (NSERC) of Canada through its Discovery Grant. The use of the Western Canada Research Grid computing services is also acknowledged.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada

    Dong Zhao & Deyi Xue

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  1. Dong Zhao
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  2. Deyi Xue
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Correspondence to Deyi Xue.

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Zhao, D., Xue, D. A multi-surrogate approximation method for metamodeling. Engineering with Computers 27, 139–153 (2011). https://doi.org/10.1007/s00366-009-0173-y

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