Galetzka, Armin ; Loukrezis, Dimitrios ; De Gersem, Herbert (2021)
Data-driven solvers for strongly nonlinear material response.
In: International Journal for Numerical Methods in Engineering, 122 (6)
doi: 10.1002/nme.6589
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Abstract This work presents a data-driven magnetostatic finite-element solver that is specifically well suited to cope with strongly nonlinear material responses. The data-driven computing framework is essentially a multiobjective optimization procedure matching the material operation points as closely as possible to given material data while obeying Maxwell's equations. Here, the framework is extended with heterogeneous (local) weighting factors—one per finite element—equilibrating the goal function locally according to the material behavior. This modification allows the data-driven solver to cope with unbalanced measurement data sets, that is, data sets suffering from unbalanced space filling. This occurs particularly in the case of strongly nonlinear materials, which constitute problematic cases that hinder the efficiency and accuracy of standard data-driven solvers with a homogeneous (global) weighting factor. The local weighting factors are embedded in the distance-minimizing data-driven algorithm used for noiseless data, likewise for the maximum entropy data-driven algorithm used for noisy data. Numerical experiments based on a quadrupole magnet model with a soft magnetic material show that the proposed modification results in major improvements in terms of solution accuracy and solver efficiency. For the case of noiseless data, local weighting factors improve the convergence of the data-driven solver by orders of magnitude. When noisy data are considered, the convergence rate of the data-driven solver is doubled.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Galetzka, Armin ; Loukrezis, Dimitrios ; De Gersem, Herbert |
| Art des Eintrags: | Bibliographie |
| Titel: | Data-driven solvers for strongly nonlinear material response |
| Sprache: | Englisch |
| Publikationsjahr: | 30 März 2021 |
| Verlag: | Wiley & Sons |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal for Numerical Methods in Engineering |
| Jahrgang/Volume einer Zeitschrift: | 122 |
| (Heft-)Nummer: | 6 |
| DOI: | 10.1002/nme.6589 |
| URL / URN: | https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.6589 |
| Kurzbeschreibung (Abstract): | Abstract This work presents a data-driven magnetostatic finite-element solver that is specifically well suited to cope with strongly nonlinear material responses. The data-driven computing framework is essentially a multiobjective optimization procedure matching the material operation points as closely as possible to given material data while obeying Maxwell's equations. Here, the framework is extended with heterogeneous (local) weighting factors—one per finite element—equilibrating the goal function locally according to the material behavior. This modification allows the data-driven solver to cope with unbalanced measurement data sets, that is, data sets suffering from unbalanced space filling. This occurs particularly in the case of strongly nonlinear materials, which constitute problematic cases that hinder the efficiency and accuracy of standard data-driven solvers with a homogeneous (global) weighting factor. The local weighting factors are embedded in the distance-minimizing data-driven algorithm used for noiseless data, likewise for the maximum entropy data-driven algorithm used for noisy data. Numerical experiments based on a quadrupole magnet model with a soft magnetic material show that the proposed modification results in major improvements in terms of solution accuracy and solver efficiency. For the case of noiseless data, local weighting factors improve the convergence of the data-driven solver by orders of magnitude. When noisy data are considered, the convergence rate of the data-driven solver is doubled. |
| Freie Schlagworte: | data-driven computing, data science, electromagnetic field simulation, noisy measurements, nonlinear material response, soft magnetic materials |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder > Theorie Elektromagnetischer Felder 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder |
| Hinterlegungsdatum: | 20 Jun 2023 11:45 |
| Letzte Änderung: | 20 Jun 2023 11:45 |
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