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Technische Universität Darmstadt

1. Loukrezis, Dimitrios. Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations.

Degree: PhD, 18 Department of Electrical Engineering and Information TechnologyElectromagnetic Field Theory (until 31.12.2018 Computational Electromagnetics Laboratory)Institute for Accelerator Science and Electromagnetic FieldsGraduate School of Computational Engineering (CE), 2019, Technische Universität Darmstadt

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. Advisors/Committee Members: De Gersem, Herbert (advisor), Römer, Ulrich (advisor).

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Loukrezis, D. (2019). Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations. (Doctoral Dissertation). Technische Universität Darmstadt. Retrieved from http://tuprints.ulb.tu-darmstadt.de/8485/

Chicago Manual of Style (16th Edition):

Loukrezis, Dimitrios. “Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations.” 2019. Doctoral Dissertation, Technische Universität Darmstadt. Accessed March 20, 2019. http://tuprints.ulb.tu-darmstadt.de/8485/.

MLA Handbook (7th Edition):

Loukrezis, Dimitrios. “Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations.” 2019. Web. 20 Mar 2019.

Vancouver:

Loukrezis D. Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations. [Internet] [Doctoral dissertation]. Technische Universität Darmstadt; 2019. [cited 2019 Mar 20]. Available from: http://tuprints.ulb.tu-darmstadt.de/8485/.

Council of Science Editors:

Loukrezis D. Adaptive approximations for high-dimensional uncertainty quantification in stochastic parametric electromagnetic field simulations. [Doctoral Dissertation]. Technische Universität Darmstadt; 2019. Available from: http://tuprints.ulb.tu-darmstadt.de/8485/

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