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Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data

Georg, Niklas ; Römer, Ulrich (2020)
Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data.
In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 33 (6)
doi: 10.1002/jnm.2776
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

Generalized Polynomial Chaos (gPC) expansions are well established for forward uncertainty propagation in many application areas. Although the associated computational effort may be reduced in comparison to Monte Carlo techniques, for instance, further convergence acceleration may be important to tackle problems with high parametric sensitivities. In this work, we propose the use of conformal maps to construct a transformed gPC basis, in order to enhance the convergence order. The proposed basis still features orthogonality properties and hence, facilitates the computation of many statistical quantities such as sensitivities and moments. The corresponding surrogate models are computed by pseudo‐spectral projection using mapped quadrature rules, which leads to an improved cost accuracy ratio. We apply the methodology to Maxwell's source problem with random input data. In particular, numerical results for a parametric finite element model of an optical grating coupler are given.

Typ des Eintrags: Artikel
Erschienen: 2020
Autor(en): Georg, Niklas ; Römer, Ulrich
Art des Eintrags: Bibliographie
Titel: Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data
Sprache: Englisch
Publikationsjahr: 2020
Ort: Chichester
Verlag: John Wiley & Sons
Titel der Zeitschrift, Zeitung oder Schriftenreihe: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
Jahrgang/Volume einer Zeitschrift: 33
(Heft-)Nummer: 6
Kollation: 15 Seiten
DOI: 10.1002/jnm.2776
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Kurzbeschreibung (Abstract):

Generalized Polynomial Chaos (gPC) expansions are well established for forward uncertainty propagation in many application areas. Although the associated computational effort may be reduced in comparison to Monte Carlo techniques, for instance, further convergence acceleration may be important to tackle problems with high parametric sensitivities. In this work, we propose the use of conformal maps to construct a transformed gPC basis, in order to enhance the convergence order. The proposed basis still features orthogonality properties and hence, facilitates the computation of many statistical quantities such as sensitivities and moments. The corresponding surrogate models are computed by pseudo‐spectral projection using mapped quadrature rules, which leads to an improved cost accuracy ratio. We apply the methodology to Maxwell's source problem with random input data. In particular, numerical results for a parametric finite element model of an optical grating coupler are given.

Freie Schlagworte: conformal maps, nanoplasmonics, polynomial chaos, surrogate modeling, uncertainty quantification
ID-Nummer: e2776
Zusätzliche Informationen:

Special Issue: Advances in Forward and Inverse Surrogate Modeling for High‐Frequency Design

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 510 Mathematik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik
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
Exzellenzinitiative
Exzellenzinitiative > Graduiertenschulen
Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE)
Hinterlegungsdatum: 31 Jan 2024 10:12
Letzte Änderung: 31 Jan 2024 10:12
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