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国际不确定性的量化期刊
影响因子: 4.911 5年影响因子: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

DOI: 10.1615/Int.J.UncertaintyQuantification.2020031727
pages 165-193

ENHANCED ADAPTIVE SURROGATE MODELS WITH APPLICATIONS IN UNCERTAINTY QUANTIFICATION FOR NANOPLASMONICS

Niklas Georg
Institut für Dynamik und Schwingungen, Technische Universität Braunschweig, Braunschweig, Germany; Centre for Computational Engineering, Technische Universität Darmstadt, Darmstadt, Germany; Institut für Teilchenbeschleunigung und Elektromagnetische Felder (TEMF), Technische Universität Darmstadt, Darmstadt, Germany
Dimitrios Loukrezis
Centre for Computational Engineering, Technische Universität Darmstadt, Darmstadt, Germany; Institut für Teilchenbeschleunigung und Elektromagnetische Felder (TEMF), Technische Universität Darmstadt, Darmstadt, Germany
Ulrich Römer
Institut für Dynamik und Schwingungen, Technische Universität Braunschweig, Schleinitzstraße 20, 38106 Braunschweig, Germany
Sebastian Schöps
Centre for Computational Engineering, Technische Universität Darmstadt, Darmstadt, Germany; Institut für Teilchenbeschleunigung und Elektromagnetische Felder (TEMF), Technische Universität Darmstadt, Darmstadt, Germany

ABSTRACT

We propose an efficient surrogate modeling technique for uncertainty quantification. The method is based on a well-known dimension-adaptive collocation scheme. We improve the scheme by enhancing sparse polynomial surrogates with conformal maps and adjoint error correction. The methodology is applied to Maxwell's source problem with random input data. This setting comprises many applications of current interest from computational nanoplasmonics, such as grating couplers or optical waveguides. Using a nontrivial benchmark model, we show the benefits and drawbacks of using enhanced surrogate models through various numerical studies. The proposed strategy allows us to conduct a thorough uncertainty analysis, taking into account a moderately large number of random parameters.

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