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Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations

Loukrezis, Dimitrios (2019)
Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations.
Technische Universität
Ph.D. Thesis, Primary publication

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Item Type: Ph.D. Thesis
Type of entry: Primary publication
Title: Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations
Language: English
Referees: De Gersem, Prof. Dr. Herbert ; Römer, Prof. Dr. Ulrich
Date: 4 February 2019
Place of Publication: Darmstadt
Date of oral examination: 4 February 2019
Abstract:

The present work addresses the problems of high-dimensional approximation and uncertainty quantification in the context of electromagnetic field simulations. In the presence of many parameters, one faces the so-called curse of dimensionality. The focus of this work lies on adaptive methods that mitigate the effect of the curse of dimensionality, and therefore enable otherwise intractable uncertainty quantification studies. Its application scope includes electromagnetic field models suffering from moderately high-dimensional input uncertainty. However, the presented methods can be used in a black-box fashion and are therefore applicable to other types of problems as well.

Alternative Abstract:
Alternative AbstractLanguage

Die vorliegende Arbeit beschäftigt sich mit den Problemen der hochdimensionalen Approximation und der Quantifizierung der Unsicherheit im Zusammenhang mit elektromagnetischen Feldsimulationen. In Anwesenheit vieler Parameter steht man dem sogenannten Fluch der Dimensionalität gegenüber. Der Fokus dieser Arbeit liegt auf adaptiven Methoden, die die Auswirkung des Fluches der Dimensionalität abschwächen und daher ansonsten unlösbare Quantifizierungsstudien zur Unsicherheit ermöglichen. Sein Anwendungsbereich umfasst elektromagnetische Feldmodelle, die unter mäßig hochdimensionaler Eingangsunsicherheit leiden. Die vorgestellten Verfahren können jedoch in einer Black-Box-Art verwendet werden und sind daher auch auf andere Arten von Problemen anwendbar.

German
URN: urn:nbn:de:tuda-tuprints-84854
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Electromagnetic Field Theory (until 31.12.2018 Computational Electromagnetics Laboratory)
18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Date Deposited: 05 Mar 2019 09:30
Last Modified: 08 Mar 2019 07:53
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8485
PPN: 445817135
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