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.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Dirk Gorissen, Wouter Hendrickx, Tom Dhaene
• Keywords: Computational Complexity, Current Simulation Codes, ESANN 2007, Multivariate Metamodeling Toolbox, Neural Networks