|Publisher DOI:||10.1109/TCPMT.2021.3056746||Title:||A multistage adaptive sampling scheme for passivity characterization of large-scale macromodels||Language:||English||Authors:||De Stefano, Marco
|Keywords:||Adaptive sampling; Hamiltonian matrices; macromodeling; model order reduction; multiscale optimization; passivity; scattering; shielding||Issue Date:||1-Mar-2021||Publisher:||IEEE||Source:||IEEE Transactions on Components, Packaging and Manufacturing Technology 11 (3): 9345758 (2021-03-01)||Abstract (english):||
This article proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. In this article, large scale is intended both in terms of dynamic order and especially number of input-output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or nonapplicable for large-scale models, due to an excessive computational cost. This article builds on existing adaptive sampling methods and proposes a hybrid multistage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches.
|URI:||http://hdl.handle.net/11420/10743||ISSN:||2156-3985||Journal:||IEEE transactions on components, packaging and manufacturing technology||Institute:||Theoretische Elektrotechnik E-18||Document Type:||Article|
|Appears in Collections:||Publications without fulltext|
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