Structural optimization based on CAD-CAE integration and metamodeling techniques
Computer-Aided Design
A comparative study of metamodeling methods considering sample quality merits
Structural and Multidisciplinary Optimization
Presence: Teleoperators and Virtual Environments
Application of artificial neural network to building compartment design for fire safety
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Numerical assessment of metamodelling strategies in computationally intensive optimization
Environmental Modelling & Software
An adaptive hybrid surrogate model
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
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The process of constructing computationally benign approximations of expensive computer simulation codes, or metamodeling, is a critical component of several large-scale multidisciplinary design optimization (MDO) approaches. Such applications typically involve complex models, such as finite elements, computational fluid dynamics, or chemical processes. The decision regarding the most appropriate metamodeling approach usually depends on the type of application. However, several newly proposed kernel-based metamodeling approaches can provide consistently accurate performance for a wide variety of applications. The authors recently proposed one such novel and effective metamodeling approach—the extended radial basis function (E-RBF) approach—and reported highly promising results. To further understand the advantages and limitations of this new approach, we compare its performance to that of the typical RBF approach, and another closely related method—kriging. Several test functions with varying problem dimensions and degrees of nonlinearity are used to compare the accuracies of the metamodels using these metamodeling approaches. We consider several performance criteria such as metamodel accuracy, effect of sampling technique, effect of sample size, effect of problem dimension, and computational complexity. The results suggest that the E-RBF approach is a potentially powerful metamodeling approach for MDO-based applications, as well as other classes of computationally intensive applications.