Fuhrländer, Mona ; Schöps, Sebastian (2022)
A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices.
In: Journal of Mathematics in Industry, 10
doi: 10.1186/s13362-020-00093-1
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Fuhrländer, Mona ; Schöps, Sebastian |
| Art des Eintrags: | Bibliographie |
| Titel: | A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Mathematics in Industry |
| Jahrgang/Volume einer Zeitschrift: | 10 |
| Kollation: | 17 Seiten |
| DOI: | 10.1186/s13362-020-00093-1 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches. |
| Zusätzliche Informationen: | Keywords: Yield analysis; Failure probability; Uncertainty quantification; Monte Carlo; Gaussian process regression; Surrogate model; Blackbox |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 510 Mathematik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder > Computational Electromagnetics 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder |
| Hinterlegungsdatum: | 02 Aug 2024 12:39 |
| Letzte Änderung: | 02 Aug 2024 12:39 |
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Verfügbare Versionen dieses Eintrags
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A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices. (deposited 08 Apr 2022 11:56)
- A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices. (deposited 02 Aug 2024 12:39) [Gegenwärtig angezeigt]
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