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An hp‐adaptive multi‐element stochastic collocation method for surrogate modeling with information re‐use

Galetzka, Armin ; Loukrezis, Dimitrios ; Georg, Niklas ; De Gersem, Herbert ; Römer, Ulrich (2023)
An hp‐adaptive multi‐element stochastic collocation method for surrogate modeling with information re‐use.
In: International Journal for Numerical Methods in Engineering, 2023, 124 (12)
doi: 10.26083/tuprints-00024293
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: An hp‐adaptive multi‐element stochastic collocation method for surrogate modeling with information re‐use
Language: English
Date: 10 November 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Place of primary publication: Chichester
Publisher: John Wiley & Sons
Journal or Publication Title: International Journal for Numerical Methods in Engineering
Volume of the journal: 124
Issue Number: 12
DOI: 10.26083/tuprints-00024293
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

This article introduces an hp‐adaptive multi‐element stochastic collocation method, which additionally allows to re‐use existing model evaluations during either h‐ or p‐refinement. The collocation method is based on weighted Leja nodes. After h‐refinement, local interpolations are stabilized by adding and sorting Leja nodes on each newly created sub‐element in a hierarchical manner. For p‐refinement, the local polynomial approximations are based on total‐degree or dimension‐adaptive bases. The method is applied in the context of forward and inverse uncertainty quantification to handle non‐smooth or strongly localized response surfaces. The performance of the proposed method is assessed in several test cases, also in comparison to competing methods.

Uncontrolled Keywords: hp‐adaptivity, multi‐element approximation, stochastic collocation, surrogate modeling, uncertainty quantification
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-242934
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 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: 10 Nov 2023 15:25
Last Modified: 05 Dec 2023 06:07
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/24293
PPN: 513347380
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