Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
dc.contributor.author | PIERQUIN, Antoine |
dc.contributor.author | HENNERON, Thomas |
dc.contributor.author
hal.structure.identifier | CLENET, Stephane
|
dc.date.accessioned | 2018 |
dc.date.available | 2018 |
dc.date.issued | 2017 |
dc.date.submitted | 2018 |
dc.identifier.issn | 0018-9464 |
dc.identifier.uri | http://hdl.handle.net/10985/12497 |
dc.description.abstract | Among the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its efficiency to solve magnetostatic and magneto-quasistatic problems in the time domain. However, the POD is intrusive in the sense that it requires the extraction of the matrix system of the full model to build the reduced model. To avoid this extraction, nonintrusive approaches like the Data Driven (DD) methods enable to approximate the reduced model without the access to the full matrix system. In this article, the DD-POD method is applied to build a low dimensional system to solve a magnetostatic problem coupled with electric circuit equations. |
dc.language.iso | en |
dc.publisher | Institute of Electrical and Electronics Engineers |
dc.rights | Post-print |
dc.subject | Data driven |
dc.subject | finite element model |
dc.subject | model order reduction |
dc.title | Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations |
dc.identifier.doi | 10.1109/TMAG.2017.2771358 |
dc.typdoc | Article dans une revue avec comité de lecture |
dc.localisation | Centre de Lille |
dc.subject.hal | Mathématique: Analyse numérique |
dc.subject.hal | Sciences de l'ingénieur: Electromagnétisme |
dc.subject.hal | Sciences de l'ingénieur: Energie électrique |
ensam.audience | Non spécifiée |
ensam.page | 1-4 |
ensam.journal | IEEE Transactions on Magnetics |
ensam.peerReviewing | Oui |
hal.identifier | hal-01691190 |
hal.version | 1 |
hal.status | accept |