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Metamodeling approach has been widely used due to the high computational cost of using high-fidelity simulations in engineering design. The accuracy of metamodels is directly related to the experimental designs used. Optimal experimental designs have been shown to have good " space filling " and projective properties. However, the high cost in constructing(More)
There are by now a plethora of artificial neural-network textbooks which attempt to cover the subject from A to Z. Most of these texts are rather bland and present superficial overviews of neural-net topics in disjointed and disparate discussions. In recent years, however, a few notable exceptions to this rule have appeared, such as the texts by Hertz et(More)
Approximation models (also known as metamodels) have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the sampling strategies used. Our goal in this paper is to investigate the general applicability(More)
Kriging is a popular analysis approach for computer experiment for the purpose of creating a cheap-to-compute "metamodel" as a surrogate to a computationally expensive engineering simulation model. The maximum likelihood approach is employed to estimate the parameters in the Kriging model. However, the likelihood function near the optimum may be flat in(More)
In this work, we propose an integrated framework for optimization under uncertainty that can bring both the design objective robustness and the probabilistic design constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective(More)
Our research is motivated by the need for developing a rigorous Decision-Based Design framework and the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring. Even though demand modeling techniques exist in market research, little work exists on product demand modeling that addresses the specific(More)
* Metamodeling approach has been widely used due to the high computational cost of using high-fidelity simulations in engineering design. Interpretation of metamodels for the purpose of design, especially design under uncertainty, becomes important. The computational expenses associated with metamodels and the random errors introduced by sample-based(More)
The performance of SVM models often depends on the proper choice of their regularized parameter(s). Some recent researches have been focusing on efficiently building all SVM models against the regularized parameters, thus defining a task of tracing the regularized piecewise linear solution path for SVMs. It has been widely known from an optimization view(More)