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International Journal of Antennas and Propagation
Volume 2014, Article ID 165102, 9 pages
Research Article

Bayesian Nonparametric Modeling for Rapid Design of Metamaterial Microstructures

1School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2Shenzhen Kuang-Chi Institute of Advanced Technology, Shenzhen 518057, China

Received 19 March 2014; Revised 18 May 2014; Accepted 21 May 2014; Published 16 June 2014

Academic Editor: Vincenzo Galdi

Copyright © 2014 Bin Liu and Chunlin Ji. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We consider the problem of rapid design of massive metamaterial (MTM) microstructures from a statistical point of view. A Bayesian nonparametric model, namely, Gaussian Process (GP) mixture, is developed to generate the mapping relationship from the microstructure’s geometric dimension to the electromagnetic response, which is approximately expressed in a closed form of Drude-Lorentz type model. This GP mixture model is neatly able to tackle nonstationarity, discontinuities in the mapping function. The inference is performed using a Markov chain relying on Gibbs sampling. Experimental results demonstrate that the proposed approach is highly efficient in facilitating rapid design of MTM with accuracy.