AIAA Journal · 2014 · 376 citations · 189 references
Natural SciencesSystems EngineeringDesign OptimizationExperimental DesignOptimizationMultiobjective OptimizationSurrogate-based OptimizationMetamodel-assisted Optimization
Metamodeling techniques have advanced significantly over the past 25 years, yet the field’s evolution remains largely driven by the same motivations as in its early days. This paper investigates how far metamodeling techniques in multidisciplinary design optimization have progressed since the seminal 1989 work by Sacks et al. The authors review current research thrusts, highlighting multifidelity approximations, ensembles of metamodels, commercial software integration, and the use of metamodels for rapid design space exploration and visualization. The paper concludes by outlining future research directions driven by the need for new capabilities and the ability to handle more complex simulations. Citation: Design and Analysis of Computer Experiments, Statistical Science, Vol.
The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past 25 years, but how far has the field really come? This is the question addressed in this paper, namely, the extent to which the use of metamodeling techniques in multidisciplinary design optimization have evolved in the 25 years since the seminal paper on design and analysis of computer experiments by Sacks et al. ( “ Design and Analysis of Computer Experiments ,” Statistical Science , Vol. 4 , No. 4 , 1989 , pp. 409 – 435 ). Rather than a technical review of the entire body of metamodeling literature, the focus is on the evolution and motivation for advancements in metamodeling with some discussion on the research itself; not surprisingly, much of the current research motivation is the same as it was in the past. Based on current research thrusts in the field, multifidelity approximations and ensembles (i.e., sets) of metamodels, as well as the availability of metamodels within commercial software, are emphasized. Design space exploration and visualization via metamodels are also presented as they rely heavily on metamodels for rapid design evaluations during exploration. The closing remarks offer insight into future research directions, mostly motivated by the need for new capabilities and the ability to handle more complex simulations.
189
A tutorial on support vector regression
Alex Smola, Bernhard Schölkopf · Statistics and Computing · 2004 · 13K citations
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Ron Kohavi · 1995 · 10.7K citations · Full text
Noel Cressie · Terra Nova · 1992 · 8.9K citations
Computing, Geography, Geoinformatics +12
Multivariate Adaptive Regression Splines
Jerome H. Friedman · The Annals of Statistics · 1991 · 8.1K citations · Full text