Machine Learning in Adaptive FETI-DP – A Comparison of Smart and Random Training Data
Conference Paper
(2020)
Author(s)
Alexander Heinlein (Center for Data and Simulation Science, Universität zu Köln)
Axel Klawonn (Universität zu Köln)
Martin Lanser (Universität zu Köln)
Janine Weber (Universität zu Köln)
Affiliation
External organisation
DOI related publication
https://doi.org/10.1007/978-3-030-56750-7_24
Final published version
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https://resolver.tudelft.nl/uuid:d686caf0-6b3e-4005-9b03-14918b9ab491
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Publication Year
2020
Language
English
Affiliation
External organisation
Pages (from-to)
218-226
Publisher
Springer
ISBN (print)
9783030567491
Event
25th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2018 (2018-07-23 - 2018-07-27), St. John's, Canada
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138
Abstract
The convergence rate of classical domain decomposition methods for diffusion or elasticity problems usually deteriorates when large coefficient jumps occur along or across the interface between subdomains. In fact, the constant in the classical condition number bounds [11, 12] will depend on the coefficient jump.