The use of computer modeling is fast increasing in almost every
scientific, engineering and business arena. This dissertation
investigates some challenging issues in design, modeling and
analysis of computer experiments, which will consist of four major
parts. In the first part, a new approach is developed to combine
data from approximate and detailed simulations to build a
surrogate model based on some stochastic models. In the second
part, we propose some Bayesian hierarchical Gaussian process
models to integrate data from different types of experiments. The
third part concerns the development of latent variable models for
computer experiments with multivariate response with application
to data center temperature modeling. The last chapter is devoted
to the development of nested space-filling designs for multiple
experiments with different levels of accuracy.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/11480 |
| Date | 19 May 2006 |
| Creators | Qian, Zhiguang |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1017052 bytes, application/pdf |
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