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 |
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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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