A Novel RBF Tuning Strategy for Global Surrogate Modeling

Wen Rui Duan; Ling Tian

doi:10.4028/www.scientific.net/AMM.670-671.881

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The Performance Analysis and Parallel Optimization of the OpenFOAM-Based Viscoelastic Solver for Heterogeneous HPC Platforms
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 670-671A Novel RBF Tuning Strategy for Global Surrogate...

A Novel RBF Tuning Strategy for Global Surrogate Modeling

Abstract:

In this paper, a research is performed on the tuning strategy for the shape parameter of the RBF to improve accuracy. The shape parameter is an influential factor for the accuracy but the configuration is commonly based on experience. An algorithm is designed to optimize the shape parameter. Three high-order mathematical benchmarks are used to compare the common RBF and the optimized RBF. This paper shows that the optimized RBF is more accurate for various test functions.

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Periodical:

Applied Mechanics and Materials (Volumes 670-671)

Pages:

881-885

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.670-671.881

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Cite this paper

Online since:

October 2014

Authors:

Wen Rui Duan, Ling Tian*

Keywords:

Optimization, RBF, Shape Parameter, Surrogate Model

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* - Corresponding Author

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