Georg, Niklas (2022)
Surrogate Modeling and Uncertainty Quantification for Radio Frequency and Optical Applications.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00021149
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
This thesis addresses surrogate modeling and forward uncertainty propagation for parametric/stochastic versions of Maxwell's source and eigenproblem. Surrogate modeling is employed to reduce the computational complexity of sampling an underlying numerical solver. First, a rational kernel-based interpolation method is developed for the efficient approximation of frequency response functions. Next, the impact of uncertain shape and material parameters is considered, which originate, for instance, in manufacturing tolerances or measurement errors. To this end, several techniques for convergence acceleration of established spectral surrogate modeling techniques, as generalized polynomial chaos or stochastic collocation, are presented. In particular, transformed basis functions are constructed based on conformal maps that suitably transform the region of holomorphy. In addition, an adjoint representation of the stochastic error is employed for an efficient dimension-adaptive scheme as well as error correction.
Several challenges arising in uncertainty quantification for radio frequency and optical components are addressed. A multifidelity scheme for an efficient and reliable yield estimation is presented which comprises sampling of a surrogate model as well as finite element models of different fidelity based on adjoint error estimation. To enable the application of spectral surrogate modeling techniques for Maxwell's eigenproblem with uncertain input data, a homotopy-based eigenvalue tracking method is proposed to ensure a consistent matching of eigenmodes. Quasi-periodic structures of finite size, subject to independent shape uncertainties, are tackled using a decoupled uncertainty propagation procedure on the unit cell level.
The methods are numerically investigated using a number of benchmark problems that encompass academic and real-world models, and their efficiency is demonstrated. Finally, comprehensive uncertainty quantification and sensitivity studies are presented for the 9-cell TESLA cavities as well as different nano-optical structures.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2022 | ||||
Autor(en): | Georg, Niklas | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Surrogate Modeling and Uncertainty Quantification for Radio Frequency and Optical Applications | ||||
Sprache: | Englisch | ||||
Referenten: | Römer, Prof. Dr. Ulrich ; Schöps, Prof. Dr. Sebastian | ||||
Publikationsjahr: | 2022 | ||||
Ort: | Darmstadt | ||||
Kollation: | xiii, 136 Seiten | ||||
Datum der mündlichen Prüfung: | 19 November 2021 | ||||
DOI: | 10.26083/tuprints-00021149 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21149 | ||||
Kurzbeschreibung (Abstract): | This thesis addresses surrogate modeling and forward uncertainty propagation for parametric/stochastic versions of Maxwell's source and eigenproblem. Surrogate modeling is employed to reduce the computational complexity of sampling an underlying numerical solver. First, a rational kernel-based interpolation method is developed for the efficient approximation of frequency response functions. Next, the impact of uncertain shape and material parameters is considered, which originate, for instance, in manufacturing tolerances or measurement errors. To this end, several techniques for convergence acceleration of established spectral surrogate modeling techniques, as generalized polynomial chaos or stochastic collocation, are presented. In particular, transformed basis functions are constructed based on conformal maps that suitably transform the region of holomorphy. In addition, an adjoint representation of the stochastic error is employed for an efficient dimension-adaptive scheme as well as error correction. Several challenges arising in uncertainty quantification for radio frequency and optical components are addressed. A multifidelity scheme for an efficient and reliable yield estimation is presented which comprises sampling of a surrogate model as well as finite element models of different fidelity based on adjoint error estimation. To enable the application of spectral surrogate modeling techniques for Maxwell's eigenproblem with uncertain input data, a homotopy-based eigenvalue tracking method is proposed to ensure a consistent matching of eigenmodes. Quasi-periodic structures of finite size, subject to independent shape uncertainties, are tackled using a decoupled uncertainty propagation procedure on the unit cell level. The methods are numerically investigated using a number of benchmark problems that encompass academic and real-world models, and their efficiency is demonstrated. Finally, comprehensive uncertainty quantification and sensitivity studies are presented for the 9-cell TESLA cavities as well as different nano-optical structures. |
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Alternatives oder übersetztes Abstract: |
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Status: | Verlagsversion | ||||
URN: | urn:nbn:de:tuda-tuprints-211498 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 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 Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
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Hinterlegungsdatum: | 13 Jul 2022 12:15 | ||||
Letzte Änderung: | 14 Nov 2022 09:57 | ||||
PPN: | 497858010 | ||||
Referenten: | Römer, Prof. Dr. Ulrich ; Schöps, Prof. Dr. Sebastian | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 19 November 2021 | ||||
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