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Titel
Basic machine learning approaches for the acceleration of PDE simulations and realization in the FEAT3 software / H. Ruelmann, M. Geveler, D. Ribbrock, P. Zajac, S. Turek
VerfasserRuelmann, Hannes ; Geveler, Markus ; Ribbrock, Dirk ; Zajac, Peter ; Turek, Stefan
ErschienenDortmund : Technische Universität Dortmund, Fakultät für Mathematik, December 2019
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (8 Seiten)
SerieErgebnisberichte angewandte Mathematik ; no. 618
SchlagwörterFinite-Elemente-Methode / Maschinelles Lernen / PDE/PROTRAN
URNurn:nbn:de:hbz:6:2-125237 
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Basic machine learning approaches for the acceleration of PDE simulations and realization in the FEAT3 software [0.13 mb]
Zusammenfassung

In this paper we present a holistic software approach based on the FEAT3 software for solving multidimensional PDEs with the Finite Element Method that is built for a maximum of performance, scalability, maintainability and extensibilty.We introduce basic paradigms howmodern computational hardware architectures such as GPUs are exploited in a numerically scalable fashion.We show, how the framework is extended to make even the most recent advances on the hardware market accessible to the framework, exemplified by the ubiquitous trend to customize chips for Machine Learning. We can demonstrate that for a numerically challenging model problem, artificial neural networks can be used while preserving a classical simulation solution pipeline through the incorporation of a neural network preconditioner in the linear solver.

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