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 
Grivet-Talocia, Stefano 
Wendt, Torben 
Yang, Cheng 
Schuster, Christian 
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
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