Solving Computational Expensive Problems with a Response Surface Surrogate Models

Jian Jun Lin; Cheng Long Liu; Yue Qiang Zhang

doi:10.4028/www.scientific.net/AMM.733.876

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Suffix Tree Constructing Algorithm for Datasets with Discrete Contents
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A Kriging Model-Based Approximation Technique for Predictive Design
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Solving Computational Expensive Problems with a Response Surface Surrogate Models
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Surrogating for High Dimensional Computationally Expensive Multi-Modal Functions with Elliptical Basis Function Models
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Study on Approximation Methods with Chebyshev Polynomial Models
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Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 733Solving Computational Expensive Problems with a...

Solving Computational Expensive Problems with a Response Surface Surrogate Models

Abstract:

Modern CAE technology is widely used in modern automobile design, engine design and so on. But its computational expense and highly time cost reduce the efficiency of design. The paper studied a respond surface model-based technique for predictive design. Firstly, we study and compare the four kinds of polynomial orders of RSM technique. Then we use Latin Hypercube technique choose enough sample points for approximation. At last, we employed cross-validation error analysis for error analysis. The results of expensive benchmark functions of CEC 2014 tested our predictive methods.

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Periodical:

Applied Mechanics and Materials (Volume 733)

Pages:

876-879

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.733.876

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Online since:

February 2015

Authors:

Jian Jun Lin, Cheng Long Liu, Yue Qiang Zhang

Keywords:

Approximation, Latin Hypercube Technique, Response Surface Model

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