We introduce a Radial Basis Function (RBF) parameterized macromodeling algorithm, specifically designed for high-dimensional parameters. As opposed to standard approaches, the adopted RBF model representation has the potential to scale very favorably when the number of model parameters increases, since the number of model coefficients is not related to the dimension of the embedding parameter space. A transmission-line example with up to seven parameters is used to demonstrate the proposed approach.

High-Dimensional Parameterized Macromodeling with Guaranteed Stability / Zanco, Alessandro; Grivet-Talocia, Stefano. - ELETTRONICO. - (2019), pp. 1-3. ((Intervento presentato al convegno 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) tenutosi a Montreal, QC, Canada nel October 6-9, 2019 [10.1109/EPEPS47316.2019.193203].

High-Dimensional Parameterized Macromodeling with Guaranteed Stability

Zanco, Alessandro;Grivet-Talocia, Stefano
2019

Abstract

We introduce a Radial Basis Function (RBF) parameterized macromodeling algorithm, specifically designed for high-dimensional parameters. As opposed to standard approaches, the adopted RBF model representation has the potential to scale very favorably when the number of model parameters increases, since the number of model coefficients is not related to the dimension of the embedding parameter space. A transmission-line example with up to seven parameters is used to demonstrate the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2817654