Research Article

You have free access to this content

Surrogate modeling of microwave structures using kriging, co-kriging, and space mapping

  1. Ivo Couckuyt1,
  2. Slawomir Koziel2,*,
  3. Tom Dhaene1

Article first published online: 15 MAR 2012

DOI: 10.1002/jnm.1833

Issue

International Journal of Numerical Modelling: Electronic Networks, Devices and Fields

International Journal of Numerical Modelling: Electronic Networks, Devices and Fields

Volume 26, Issue 1, pages 64–73, January/February 2013

Additional Information

How to Cite

Couckuyt, I., Koziel, S. and Dhaene, T. (2013), Surrogate modeling of microwave structures using kriging, co-kriging, and space mapping. Int. J. Numer. Model., 26: 64–73. doi: 10.1002/jnm.1833

Author Information

  1. 1

    Surrogate Modeling Lab, Department of Information Technology, Ghent University-IBBT, Ghent, Belgium

  2. 2

    Engineering Optimization & Modeling Center, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland

* Slawomir Koziel, Engineering Optimization & Modeling Center, School of Science and Engineering, Reykjavik University, Menntavegur 1, 101 Reykjavik, Iceland.
E-mail: koziel@ru.is

Publication History

  1. Issue published online: 10 DEC 2012
  2. Article first published online: 15 MAR 2012

Funded by

  • Icelandic Centre for Research (RANNIS). Grant Number: 110034021

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

View Full Article (HTML) Get PDF (379K)

REFERENCES

  • 1
    Forrester AIJ, Keane AJ. Recent advances in surrogate-based optimization. Prog Aerosp Sci Jan.-April 2009; 45(1–3):50–79.
  • 2
    Buhmann MD, Ablowitz MJ. Radial Basis Functions: Theory and Implementations. Cambridge University Press: Cambridge, MA, 2003.
  • 3
    Simpson TW, Peplinski J, Koch PN, Allen JK. Metamodels for computer-based engineering design: survey and recommendations. Eng Comput July 2001; 17(2):129–150.
  • 4
    Queipo NV, Haftka RT, Shyy W, Goel T, Vaidynathan R, Tucker PK. Surrogate-based analysis and optimization. Prog Aerosp Sci Jan. 2005; 41(1):1–28.
  • 5
    Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput Aug 2004; 14(3):199–222.
  • 6
    Meng J, Xia L. Support-vector regression model for millimeter wave transition. Int. J. Infrared Mili Waves 2007; May 28(5):413–421.
  • 7
    Xia L, Meng J, Xu R, Yan B, Guo Y. Modeling of 3-D vertical interconnect using support vector machine regression. IEEE Microw Wirel Comput Lett Dec 2006; 16(12):639–641.
  • 8
    Martinez-Ramon M, Christodoulou C. Support vector machines for antenna array processing and electromagnetics. Synth Lect Comput Electromagn 2006; 1(1):1–5120.
  • 9
    Miraftab V, Mansour RR. EM-based microwave circuit design using fuzzy logic techniques. IEE Proc Microw Anten Propag Dec 2006; 153(6):495–501.
  • 10
    Zhai J, Zhou J, Zhang L, Hong W. Behavioral modeling of power amplifiers with dynamic fuzzy neural networks. IEEE Microw Wirel Comput Lett 2010; 20(9):528–530.
  • 11
    Shaker GSA, Bakr MH, Sangary N, Safavi-Naeini S. Accelerated antenna design methodology exploiting parameterized Cauchy models. Progress in Electromagnetic Research. 2009; 20:279–309.
  • 12
    Rayas-Sánchez JE. EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art. IEEE Trans Microw Theory Tech Jan 2004; 52(1):420–435.
  • 13
    Kabir H, Wang Y, Yu M, Zhang QJ. Neural network inverse modeling and applications to microwave filter design. IEEE Trans Microw Theory Tech April 2008; 56(4):867–879.
  • 14
    Cao Y, Chen X, Wang G. Dynamic behavioral modeling of nonlinear microwave devices using real-time recurrent neural network. IEEE Trans Electron Devices 2009; 56(5):1020–1026.
  • 15
    Kabir H, Wang Y, Yu M, Zhang QJ. High-dimensional neural-network technique and applications to microwave filter modeling. IEEE Trans Microw Theory Tech Jan 2010; 58(1):145–156.
  • 16
    Bandler JW, Georgieva N, Ismail MA, Rayas-Sánchez JE, Zhang QJ. A generalized space mapping tableau approach to device modeling. IEEE Trans Microw Theory Tech Jan 2001; 49(1):67–79.
  • 17
    Rayas-Sánchez E, Gutierrez-Ayala V. EM-based Monte Carlo analysis and yield prediction of microwave circuits using linear-input neural-output space mapping. IEEE Trans Microw Theory Tech 2006; 54(12):4528–4537.
  • 18
    Koziel S, Bandler JW. Recent advances in space-mapping-based modeling of microwave devices. Int J Numer Model 2010; 23(6):425–446.
    Direct Link:
  • 19
    Zhang L, Zhang QJ, Wood J. Statistical neuro-space mapping technique for large-signal modeling of nonlinear devices. IEEE Trans Microw Theory Tech 2011; 56(1):2453–2467.
  • 20
    Bandler JW, Cheng QS, Koziel S. Simplified space mapping approach to enhancement of microwave device models. Int. J. RF Microw Comput-Aid Eng. 2006; 16(5):518–535.
    Direct Link:
  • 21
    Koziel S, Bandler JW. A space-mapping approach to microwave device modeling exploiting fuzzy systems. IEEE Trans Microw Theory Tech Dec 2007; 55(12):2539–2547.
  • 22
    Koziel S, Bandler JW, Madsen K. Theoretical justification of space-mapping-based modeling utilizing a data base and on-demand parameter extraction. IEEE Trans Microw Theory Tech Dec 2006; 54(12):4316–4322.
  • 23
    Koziel S, Bandler JW. Accurate modeling of microwave devices using space mapping and kriging. International Review of Progress in Applied Computational Electromagnetics, April 26–29, Tampere, Finland, 2010:902–907.
  • 24
    Sacks J, Welch WJ, Mitchell T, Wynn HP. Design and analysis of computer experiments. Stat sci 1989; 4(4):409–435.
  • 25
    Kennedy MC, O'Hagan A. Predicting the output from complex computer code when fast approximations are available. Biometrika 2000; 87:1–13.
  • 26
    Koziel S, Bandler JW, Madsen K. A space mapping framework for engineering optimization: theory and implementation. IEEE Trans Microw Theory Tech 2006; 54(10):3721–3730.
  • 27
    Bandler JW, Cheng QS, Nikolova NK, Ismail MA. Implicit space mapping optimization exploiting preassigned parameters. IEEE Trans Microw Theory Tech Jan 2004; 52(1):378–385.
  • 28
    Cheng QS, Bandler JW, Koziel S. An accurate microstrip hairpin filter design using implicit space mapping. Microw Magazine Feb 2008; 9(1):79–88.
  • 29
    Gorissen D, Crombecq K, Couckuyt I, Demeester P, Dhaene T. A surrogate modeling and adaptive sampling toolbox for computer based design. J Mach Learn Res 2010; 11:2051–2055.
  • 30
    Salleh MHM, Prigent G, Pigaglio O, Crampagne R. Quarter-wavelength side-coupled ring resonator for bandpass filters. IEEE Trans Microw Theory Tech Jan 2008; 56(1):156–162.
  • 31
    Lee JR, Cho JH, Yun SW. New compact bandpass filter using microstrip λ/4 resonators with open stub inverter. IEEE Microw Guided Wave Lett Dec 2000; 10(12):526–527.
  • 32
    FEKO, Suite 5.5, EM Software & Systems-S.A. (Pty) Ltd, 32 Techno Lane, Technopark, Stellenbosch, 7600, South Africa, 2009.
  • 33
    Agilent ADS, Version 2008, Agilent Technologies, 1400 Fountaingrove Parkway, Santa Rosa, CA 2008 95403–1799.
  • 34
    Koziel S, Echeverría Ciaurri D. Reliable simulation-driven design optimization of microwave structures using manifold mapping. Prog Electromagn Res B (PIER B) 2010; 26:361–382.
  • 35
    Jones DR, Schonlau M, Welch WJ. Efficient global optimization of expensive black-box functions. J Global Optim 1998; 13:455–492.
  • 36
    Couckuyt I, Declercq F, Dhaene T, Rogier H. Surrogate-based infill optimization applied to electromagnetic problems. Advances in design optimization of microwave/rf circuits and systems 2010; 20:492.
  • 37
    Wang G, Shan S. Review of metamodeling techniques in support of engineering design optimization. J Mech Desi, ASME 2007; 129:370–380.
  • 38
    Rasmussen CE, Williams CKI. Gaussian Processes for Machine Learning. MIT Press: Cambridge, MA, 2006.
  • 39
    Forrester AI, Sobester A, Keane AJ. Multi-fidelity optimization via surrogate modelling. Royal Society Proc Roy Soc 2007; 463:3251–3269.
  • 40
    Stein ML. Interpolation of Spatial Data: Some Theory for Kriging. Springer-Verlag: Berlin, 1999.
  • 41
    J Staum. Better simulation metamodeling: the why, what, and how of stochastic kriging. Proceedings of the 2009 Winter Simulation Conference, 119–133, 2009.
View Full Article (HTML) Get PDF (379K)