Computational Improvements to Estimating Kriging Metamodel Parameters
| Autor: | Jay D. Martin |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Hessian matrix
Mathematical optimization Engineering Estimation theory business.industry Mechanical Engineering Gaussian Computer Graphics and Computer-Aided Design Computer Science Applications Metamodeling symbols.namesake Surrogate model Mechanics of Materials Kriging Scoring algorithm symbols Variogram business |
| Zdroj: | Journal of Mechanical Design. 131 |
| ISSN: | 1528-9001 1050-0472 |
| DOI: | 10.1115/1.3151807 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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