Loukrezis, Dimitrios ; Galetzka, Armin ; De Gersem, Herbert (2023)
Robust adaptive least squares polynomial chaos expansions in high‐frequency applications.
In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2020
doi: 10.26083/tuprints-00015968
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
We present an algorithm for computing sparse, least squares‐based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The algorithm is employed to approximate stochastic high‐frequency electromagnetic models in a black‐box way, in particular, given only a dataset of random parameter realizations and the corresponding observations regarding a quantity of interest, typically a scattering parameter. The construction of the polynomial basis is based on a greedy, adaptive, sensitivity‐related method. The sequential expansion of the experimental design employs different optimality criteria, with respect to the algebraic form of the least squares problem. We investigate how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs. It is found that relatively optimistic criteria perform on average better than stricter ones, yielding superior approximation accuracies for equal dataset sizes. However, the results of strict criteria are significantly more robust, as reduced variations regarding the approximation accuracy are obtained, over a range of experimental designs. Two criteria are proposed for a good accuracy‐robustness trade‐off.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Loukrezis, Dimitrios ; Galetzka, Armin ; De Gersem, Herbert |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Robust adaptive least squares polynomial chaos expansions in high‐frequency applications |
| Sprache: | Englisch |
| Publikationsjahr: | 4 Dezember 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Ort der Erstveröffentlichung: | Chichester |
| Verlag: | Wiley |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Numerical Modelling: Electronic Networks, Devices and Fields |
| Kollation: | 15 Seiten |
| DOI: | 10.26083/tuprints-00015968 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/15968 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | We present an algorithm for computing sparse, least squares‐based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The algorithm is employed to approximate stochastic high‐frequency electromagnetic models in a black‐box way, in particular, given only a dataset of random parameter realizations and the corresponding observations regarding a quantity of interest, typically a scattering parameter. The construction of the polynomial basis is based on a greedy, adaptive, sensitivity‐related method. The sequential expansion of the experimental design employs different optimality criteria, with respect to the algebraic form of the least squares problem. We investigate how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs. It is found that relatively optimistic criteria perform on average better than stricter ones, yielding superior approximation accuracies for equal dataset sizes. However, the results of strict criteria are significantly more robust, as reduced variations regarding the approximation accuracy are obtained, over a range of experimental designs. Two criteria are proposed for a good accuracy‐robustness trade‐off. |
| Freie Schlagworte: | adaptive basis, high‐frequency electromagnetic devices, least squares regression, polynomial chaos, sequential experimental design, surrogate modeling |
| ID-Nummer: | e2725 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-159685 |
| Zusätzliche Informationen: | Special Issue: Advances in Forward and Inverse Surrogate Modeling for High‐Frequency Design |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 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 Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 04 Dez 2023 13:49 |
| Letzte Änderung: | 08 Dez 2023 13:09 |
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| Suche nach Titel in: | TUfind oder in Google |
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