Georg, Niklas (2022)
Surrogate Modeling and Uncertainty Quantification for Radio Frequency and Optical Applications.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00021149
Ph.D. Thesis, Primary publication, Publisher's Version
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Surrogate Modeling and Uncertainty Quantification for Radio Frequency and Optical Applications | ||||
Language: | English | ||||
Referees: | Römer, Prof. Dr. Ulrich ; Schöps, Prof. Dr. Sebastian | ||||
Date: | 2022 | ||||
Place of Publication: | Darmstadt | ||||
Collation: | xiii, 136 Seiten | ||||
Date of oral examination: | 19 November 2021 | ||||
DOI: | 10.26083/tuprints-00021149 | ||||
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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Status: | Publisher's Version | ||||
URN: | urn:nbn:de:tuda-tuprints-211498 | ||||
Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering | ||||
Divisions: | 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Computational Electromagnetics 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE) |
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Date Deposited: | 13 Jul 2022 12:15 | ||||
Last Modified: | 14 Nov 2022 09:56 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/21149 | ||||
PPN: | 497858010 | ||||
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