Article2008

Blind Kriging: A New Method for Developing Metamodels

V. Roshan Joseph · Ying Hung · Agus Sudjianto

264 citations0/0 files storedJournal of Mechanical Design

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Optimal Experimental Design Methods Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

Abstract

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore, it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented, which show remarkable improvement in prediction using blind kriging over ordinary kriging. Moreover, a blind kriging predictor is easier to interpret and seems to be more robust against mis-specification in the correlation parameters.

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References

1989Design and Analysis of Computer Experiments 2005Design and Modeling for Computer Experiments 2001Experiments - Planning, Analysis and Parameter Design Optimization 1989Designs for Computer Experiments 2005Use of Kriging Models to Approximate Deterministic Computer Models 1992Screening, Predicting, and Computer Experiments 2021Experiments 1992Analysis of Designed Experiments with Complex Aliasing

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