The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past two decades, but how far have we really come? This is the question that we investigate in this paper, namely, the extent to which the use of metamodeling techniques in multidisciplinary design optimization have evolved in the two decades since the seminal paper on Design and Analysis of Computer Experiments by Sacks et al. As part of this review, we examine the motivation for advancements in metamodeling techniques from both a historical perspective and the research itself. Based on current thrusts in the field, we emphasize multi-level/multi-fidelity approximations and ensembles of metamodels, as well as the availability of metamodels within commercial software and for design space exploration and visualization in this review. Our closing remarks offer insight into future research directions-nearly the same ones that have motivated us in the past. I.

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A procedure for generating a customized weight function for wing bending material weight of the High Speed Civil Transport (HSCT) is described. The weight function is based on the shape parameters. A response surface methodology is used to t a quadratic polynomial to data gathered from a large number of structural optimizations. The results of the structural optimization are noisy. Noise reduction in the structural optimization results is discussed. Several techniques are used to minimize the number of required structural optimizations and to maintain accuracy. Simple analysis techniques are used to nd regions of the design space where reasonable HSCT designs could occur, thus customizing the weight function to the design requirements of the HSCT, while the response surfaces themselves are created employing detailed analysis methods. Intervening variables and analysis of variance are used to reduce the number of polynomial terms in the response surface model functions. Minimum variance and minimum bias procedures for creation of response surfaces are compared. Con guration optimization of the HSCT employing customized weight functions with di erent response surfaces are compared.

ii Acknowledgements