A Nonparametric Bootstrap Approach to Variable-size Local-domain Design Optimization and Computer Model Validation 2012-01-0226
Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, a recent approach was proposed where design optimization and model validation were concurrently performed using a sequential approach with both fixed and variable-size local domains. The variable-size approach used parametric distributions such as Gaussian to quantify the variability in test data and model predictions, and a maximum likelihood estimation to calibrate the prediction model. Also, a parametric bootstrap method was used to size each local domain. In this article, we generalize the variable-size approach, by not assuming any distribution such as Gaussian. A nonparametric bootstrap methodology is instead used to size the local domains. We expect its generality to be useful in applications where distributional assumptions are difficult to verify, or not met at all. A heat conduction problem illustrates the proposed methodology.
Citation: Drignei, D., Mourelatos, Z., Pandey, V., and Kokkolaras, M., "A Nonparametric Bootstrap Approach to Variable-size Local-domain Design Optimization and Computer Model Validation," SAE Int. J. Mater. Manf. 5(1):143-149, 2012, https://doi.org/10.4271/2012-01-0226. Download Citation
Dorin Drignei, Zissimos Mourelatos, Vijitashwa Pandey, Michael Kokkolaras
Oakland Univ., Univ. of Michigan-Ann Arbor
SAE 2012 World Congress & Exhibition
SAE International Journal of Materials and Manufacturing-V121-5, SAE International Journal of Materials and Manufacturing-V121-5EJ