Abstract
The finite element method is a numerical simulation technique for solving partial differential equations. Domain decomposition provides a means for parallelizing the expensive simulation with modern computing architecture. Choosing the sub-domains for domain decomposition is a non-trivial task, and in this paper we show how this can be addressed with machine learning. Our method starts with a baseline decomposition, from which we learn tailored sub-domain overlaps from localized neighborhoods. An evaluation of 527 partial differential equations shows that our learned solutions improve the baseline decomposition with high consistency and by a statistically significant margin.
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Burrows, S., Frochte, J., Völske, M., Torres, A.B.M., Stein, B. (2013). Learning Overlap Optimization for Domain Decomposition Methods. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_36
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DOI: https://doi.org/10.1007/978-3-642-37453-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37452-4
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