This work was supported by the Research Foundation Flanders (FWO).
Gradient-based optimization using parametric sensitivity macromodels†
Article first published online: 20 JAN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
Volume 25, Issue 4, pages 347–361, July/August 2012
How to Cite
Chemmangat, K., Ferranti, F., Dhaene, T. and Knockaert, L. (2012), Gradient-based optimization using parametric sensitivity macromodels. Int. J. Numer. Model., 25: 347–361. doi: 10.1002/jnm.839
- Issue published online: 18 JUN 2012
- Article first published online: 20 JAN 2012
- Manuscript Accepted: 11 OCT 2011
- Manuscript Revised: 26 AUG 2011
- Manuscript Received: 19 MAY 2011
- gradient-based design optimization;
- parametric sensitivity;
- parametric macromodeling;
A new method for gradient-based optimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters, such as layout and substrate parameters. A set of frequency-dependent rational models is built at a set of design space points by using the vector fitting method and converted into a state-space form. Then, this set of state-space matrices is parameterized with a proper choice of interpolation schemes, such that parametric sensitivity macromodels can be computed. These parametric macromodels, along with the corresponding parametric sensitivity macromodels, can be used in a gradient-based design optimization process. The importance of parameterized sensitivity information for an efficient and accurate design optimization is shown in the two numerical microwave examples. Copyright © 2012 John Wiley & Sons, Ltd.