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PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: B.H.V. Topping and Y. Tsompanakis
Automated Response Surface Model Generation with Sequential Design
I. Couckuyt1, K. Crombecq2, D. Gorissen1 and T. Dhaene1
1Department of Information Technology (INTEC-IBBT), Ghent University, Belgium
I. Couckuyt, K. Crombecq, D. Gorissen, T. Dhaene, "Automated Response Surface Model Generation with Sequential Design", in B.H.V. Topping, Y. Tsompanakis, (Editors), "Proceedings of the First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 52, 2009. doi:10.4203/ccp.92.52
Keywords: expected improvement, surrogate model, metamodel, optimization, sequential design, adaptive sampling, application, electro-magnetics.
For many problems from science, and engineering it is impractical to perform experiments on the physical world directly (e.g. airfoil design, earthquake propagation). Instead, complex, physics-based simulation codes are used to run experiments on computer hardware. While allowing scientists more flexibility to study phenomena under controlled conditions, computer experiments require a substantial investment in computation time. This is especially evident for routine tasks such as optimization, sensitivity analysis and design space exploration .
As a result researchers have turned to various approximation methods that mimic the behavior of the simulation model as closely as possible while being computationally less expensive to evaluate. This work concentrates on the use of data-driven approximations using compact surrogate models (also known as metamodels, or response surface models (RSM)). Examples of surrogate models include: rational functions, Gaussian process (GP) models, and support vector machines (SVM).
In particular, this work describes a popular surrogate modelling technique for optimization of expensive blackbox simulators, i.e., expected improvement . The results of this research have been incorporated in a flexible and integrated research platform, the surrogate modelling (SUMO) toolbox . Consequently the toolbox is afterwards applied to an optimization problem from electro-magnetics (EM) to design an inter-digital filter .
Results show that designs obtained through surrogate modelling techniques, which require no domain specific knowledge of the application at hand, are competitive to a reference design constructed by a domain expert, using a combination of analytic methods, circuit simulation and finite element methods.
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