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Gradient‐based optimization using parametric sensitivity macromodels
Author(s) -
Chemmangat Krishnan,
Ferranti Francesco,
Dhaene Tom,
Knockaert Luc
Publication year - 2012
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.839
Subject(s) - parameterized complexity , parametric statistics , sensitivity (control systems) , interpolation (computer graphics) , set (abstract data type) , state space , mathematical optimization , mathematics , algorithm , computer science , electronic engineering , engineering , artificial intelligence , motion (physics) , programming language , statistics
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.