Many design optimization problems rely on simulation models to obtain feasible solutions. Even with substantial improvement in the computational capability of computers, the enormous cost of computation needed for simulation makes it impractical to rely on simulation models. The use of metamodels or surrogate approximations in place of actual simulation models makes analysis realistic by reducing computational burden. There are many popular metamodeling techniques such as Polynomial Regression, Multivariate Adaptive Regression Splines, Radial Basis Functions, Kriging and Artificial Neural Networks. This research proposes a new metamodeling technique that uses Bezier curves and patches. The Bezier curve method is based on interpolation like Kriging and Radial Basis Functions. In this research the Bezier Curve method will be used for output modeling of univariate and bivariate output modeling. Results will be validated using comparison with some of the most popular metamodeling techniques.
Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-11022004-225207 |
Date | 17 November 2004 |
Creators | Kingre, Harish J |
Contributors | Xiaoyue Jiang, Charles McAllister, Bhaba.R. Sarker, James P. Geaghan |
Publisher | LSU |
Source Sets | Louisiana State University |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | http://etd.lsu.edu/docs/available/etd-11022004-225207/ |
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