Machine Learning in Adaptive FETI-DP: Reducing the Effort in Sampling

Author:

Heinlein Alexander,Klawonn Axel,Lanser Martin,Weber Janine

Publisher

Springer International Publishing

Reference14 articles.

1. I. Goodfellow, Y. Bengio, and A. Courville. Deep learning, volume 1. MIT press Cambridge, 2016.

2. A. Heinlein, A. Klawonn, J. Knepper, and O. Rheinbach. An adaptive GDSW coarse space for two-level overlapping Schwarz methods in two dimensions. 2019. Proceedings of the International Conference on Domain Decomposition Methods 24, Springer LNCSE, Vol. 125, January 2019, pp. 373–382. https://doi.org/10.1007/978-3-319-93873-8_35.

3. A. Heinlein, A. Klawonn, J. Knepper, and O. Rheinbach. Multiscale coarse spaces for overlapping Schwarz methods based on the ACMS space in 2D. Electronic Transactions on Numerical Analysis (ETNA), 48:156–182, 2018.

4. A. Heinlein, A. Klawonn, M. Lanser, and J. Weber. Machine Learning in Adaptive FETI-DP - A Comparison of Smart and Random Training Data. 2018. TR series, Center for Data and Simulation Science, University of Cologne, Germany, Vol. 2018-5. http://kups.ub.uni-koeln.de/id/eprint/8645. Accepted for publication in the proceedings of the International Conference on Domain Decomposition Methods 25, Springer LNCSE, May 2019.

5. A. Heinlein, A. Klawonn, M. Lanser, and J. Weber. Machine Learning in Adaptive Domain Decomposition Methods - Predicting the Geometric Location of Constraints. SIAM J. Sci. Comput., 41(6):A3887–A3912, 2019.

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