The details of a method to reduce the computational burden experienced while estimating the optimal model parameters for a Kriging model are presented. A Kriging model is a type of surrogate model that can be used to create a response surface based a set of observations of a computationally expensive system design analysis. This Kriging model can then be used as a computationally efficient surrogate to the original model, providing the opportunity for the rapid exploration of the resulting tradespace. The Kriging model can provide a more complex response surface than the more traditional linear regression response surface through the introduction of a few terms to quantify the spatial correlation of the observations. Implementation details and enhancements to gradient-based methods to estimate the model parameters are presented. It concludes with a comparison of these enhancements to using maximum likelihood estimation to estimate Kriging model parameters and their potential reduction in computational burden. These enhancements include the development of the analytic gradient and Hessian for the log-likelihood equation of a Kriging model that uses a Gaussian spatial correlation function. The suggested algorithm is similar to the SCORING algorithm traditionally used in statistics.
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August 2009
Technical Briefs
Computational Improvements to Estimating Kriging Metamodel Parameters
Jay D. Martin
Jay D. Martin
Research Associate
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Jay D. Martin
Research Associate
J. Mech. Des. Aug 2009, 131(8): 084501 (7 pages)
Published Online: July 9, 2009
Article history
Received:
September 13, 2007
Revised:
March 25, 2009
Published:
July 9, 2009
Citation
Martin, J. D. (July 9, 2009). "Computational Improvements to Estimating Kriging Metamodel Parameters." ASME. J. Mech. Des. August 2009; 131(8): 084501. https://doi.org/10.1115/1.3151807
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