TU Darmstadt / ULB / TUprints

Data‐driven model‐free modified nodal analysis circuit solver

Galetzka, Armin ; Loukrezis, Dimitrios ; De Gersem, Herbert (2024)
Data‐driven model‐free modified nodal analysis circuit solver.
In: International Journal of Numerical Modelling : Electronic Networks, Devices and Fields, 2024, 37 (2)
doi: 10.26083/tuprints-00027181
Article, Secondary publication, Publisher's Version

[img] Text
JNM_JNM3205.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (2MB)
Item Type: Article
Type of entry: Secondary publication
Title: Data‐driven model‐free modified nodal analysis circuit solver
Language: English
Date: 4 June 2024
Place of Publication: Darmstadt
Year of primary publication: March 2024
Place of primary publication: Chichester
Publisher: John Wiley & Sons
Journal or Publication Title: International Journal of Numerical Modelling : Electronic Networks, Devices and Fields
Volume of the journal: 37
Issue Number: 2
Collation: 16 Seiten
DOI: 10.26083/tuprints-00027181
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

This work introduces a novel data‐driven model‐free modified nodal analysis (MNA) circuit solver. The solver is capable of handling circuit problems featuring elements for which solely measurement data are available. Rather than utilizing hard‐coded phenomenological model representations, the data‐driven MNA solver reformulates the circuit problem such that the solution is found by minimizing the distance between circuit states that fulfill Kirchhoff's laws, and states belonging to the measurement data. In this way, the formerly inevitable demand for model representations is eliminated, thus avoiding the introduction of related modeling errors and uncertainties. The proposed solver is applied to linear and nonlinear RC‐circuits and to a half‐wave rectifier.

Uncontrolled Keywords: circuit simulation, data‐driven computing, model‐free solver, modified nodal analysis
Identification Number: Artikel-ID: e3205
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-271810
Classification DDC: 600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Date Deposited: 04 Jun 2024 12:28
Last Modified: 18 Sep 2024 06:52
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/27181
PPN: 518869504
Export:
Actions (login required)
View Item View Item