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2007

Adaptive Global Metamodeling with Neural Networks

8 years 10 months ago
Adaptive Global Metamodeling with Neural Networks
Due to the scale and computational complexity of current simulation codes, metamodels (or surrogate models) have become indispensable tools for exploring and understanding the design space. Consequently, there is great interest in techniques that facilitate the construction and evaluation of such approximation models while minimizing the computational cost and maximizing metamodel accuracy. This paper presents an adaptive, integrated approach to global metamodeling based on the Multivariate Metamodeling Toolbox. An adaptive, evolutionary inspired, modeling algorithm based on neural networks is presented and its performance compared with rational metamodeling on a number of test problems.
Dirk Gorissen, Wouter Hendrickx, Tom Dhaene
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Dirk Gorissen, Wouter Hendrickx, Tom Dhaene
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