Machine Learning in Adaptive FETI-DP – A Comparison of Smart and Random Training Data
Conference Paper
(2020)
Author(s)
A. Heinlein (University of Cologne, Center for Data and Simulation Science)
Axel Klawonn (University of Cologne)
Martin Lanser (University of Cologne)
Janine Weber (University of Cologne)
Affiliation
External organisation
DOI related publication
https://doi.org/10.1007/978-3-030-56750-7_24
To reference this document use:
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
ISBN (print)
9783030567491
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.
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