The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.
An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 31, 2013; final manuscript received November 12, 2013; published online January 10, 2014. Assoc. Editor: Wei Chen.
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Shi, L., Yang, R., and Zhu, P. (January 10, 2014). "An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function." ASME. J. Mech. Des. March 2014; 136(3): 031005. https://doi.org/10.1115/1.4026095
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