We present a general framework for the fully automated extraction of stable and passive parameterized macromodels from sampled frequency responses. The proposed iterative algorithm provides an automated selection of the optimal parameter configurations to be simulated by a field solver, based on a combination of data-driven and model-driven metrics. The resulting frequency responses are fitted by a parameterized rational macromodel, whose uniform stability and passivity are enforced. We demonstrate the effectiveness of this framework on a transmission-line network test case.

An Adaptive Algorithm for Fully Automated Extraction of Passive Parameterized Macromodels / Fevola, E.; Zanco, A.; Grivet-Talocia, S.; Bradde, T.; De Stefano, M.. - ELETTRONICO. - (2019), pp. 1-4. ((Intervento presentato al convegno 2019 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2019 tenutosi a Boston (MA) USA nel 29-31 May 2019 [10.1109/NEMO.2019.8853720].

An Adaptive Algorithm for Fully Automated Extraction of Passive Parameterized Macromodels

Fevola E.;Zanco A.;Grivet-Talocia S.;Bradde T.;De Stefano M.
2019

Abstract

We present a general framework for the fully automated extraction of stable and passive parameterized macromodels from sampled frequency responses. The proposed iterative algorithm provides an automated selection of the optimal parameter configurations to be simulated by a field solver, based on a combination of data-driven and model-driven metrics. The resulting frequency responses are fitted by a parameterized rational macromodel, whose uniform stability and passivity are enforced. We demonstrate the effectiveness of this framework on a transmission-line network test case.
978-1-5386-9516-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2773095
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