volume | PIER Journals

Abstract

Several computer codes with varying accuracy from rigorous full-wave methods (highfidelity models) to less accurate Transmission Line (TL) approaches (low-fidelity model) have been proposed to solve EMC problems of interference between parasitic waves and wired communication systems. For solving engineering tasks, with a limited computational budget, we need to build surrogate models of high-fidelity (HF) computer codes. However, one of their main limitations is their expensive computational time. Rather than using only the computationally costly HF simulations, we apply another type of surrogate models, called Multifidelity (MF) metamodel which efficiently combines, within a Bayesian framework, high and low-fidelity (LF) evaluations to speed up the surrogate model building. The numerical results of combination of an expensive EMC simulator and a cheap TL code to solve a plane wave illumination problem, show that, compared to Kriging, a reliable Bayesian MF metamodel of equivalent or higher predictivity can be obtained within less simulation time.

1. Tesche, F., M. Ianoz, and T. Karlsson, EMC Analysis Methods and Computational Models, John Wiley & Sons, 1997.

2. Burke, G. J. and A. J. Poggio, "Numerical Electromagnetic Code (NEC)-method of moments," Technical Report, Naval Ocean Systems Center, San Diego, 1981. Google Scholar

3. Bandler, J., R. Biernacki, S. Chen, P. Grobelny, and R. Hemmers, "Space mapping technique for electromagnetic optimization," IEEE Transactions on Microwave Theory and Techniques, Vol. 42, No. 12, 2536-2544, Dec. 1994.
doi:10.1109/22.339794 Google Scholar

4. Koziel, S. and J. W. Bandler, "Space-mapping modelling of microwave devices using multifidelity electromagnetic simulations," IET Microwaves, Antennas & Propagation, Vol. 5, No. 3, 324-333, Feb. 2011.
doi:10.1049/iet-map.2010.0058 Google Scholar

5. Krige, D. G., "A statistical approach to some mine valuations problems at the Witwatersrand," Journal of the Chemical, Metallurgical and Mining Society of South Africa, Vol. 52, 119-139, 1951. Google Scholar

6. Lophaven, S. N., H. B. Nielsen, and J. Søndergaard, "Aspects of the Matlab Toolbox DACE,", Report IMM-REP-2002-13, Informatics and Mathematical Modelling, DTU, 44 pages, 2002. Google Scholar

7. Kennedy, M. C. and A. O'Hagan, "Predicting the output of a complex computer code when fast approximations are available," Biometrika, Vol. 87, No. 1, 1-13, 2000.
doi:10.1093/biomet/87.1.1 Google Scholar

8. Forrester, A. I. J., A. Sobester, and A. J. Keane, "Multifidelity optimization via surrogate modelling," Journal of Mechanical Engineering Science, Vol. 463, 2115-2137, 2007. Google Scholar

9. Bradley, P. J., "A multifidelity based adaptive sampling optimisation approach for the rapid design of double-negative metamaterials," Progress In Electromagnetics Research B, Vol. 55, 87-114, 2013.
doi:10.2528/PIERB13071003 Google Scholar

10. Le Gratiet, L., "Bayesian analysis of hierarchical multifidelity codes," SIAM/ASA J. Uncertainty Quantification, Vol. 1, No. 1, 244-269, 2013.
doi:10.1137/120884122 Google Scholar

11. Lugrin, G., N. Mora, F. Rachidi, and S. Tkachenko, "Electromagnetic field coupling to transmission lines: A model for the risers," 2016 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC), 174-176, Shenzhen, 2016.
doi:10.1109/APEMC.2016.7523000 Google Scholar

12. Sacks, J., W. J. Welch, T. J. Mitchell, and H. P. Wynn, "Design and analysis of computer experiments," Statistical Science, Vol. 4, No. 4, 409-435, 1989.
doi:10.1214/ss/1177012413 Google Scholar

13. Jacobs, J. P., S. Koziel, and S. Ogurtsov, "Computationally efficient multi-fidelity Bayesian support vector regression modeling of planar antenna input characteristics," IEEE Transactions on Antennas and Propagation, Vol. 61, No. 2, 980-984, Feb. 2013.
doi:10.1109/TAP.2012.2220513 Google Scholar

Dr. Tarek Bdour

Dr. Christopher Guiffaut

Dr. Alain Reineix