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2021 | OriginalPaper | Buchkapitel

Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software

verfasst von : Hannes Ruelmann, Markus Geveler, Dirk Ribbrock, Peter Zajac, Stefan Turek

Erschienen in: Numerical Mathematics and Advanced Applications ENUMATH 2019

Verlag: Springer International Publishing

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Abstract

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 extensibility. We introduce basic paradigms how modern 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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Literatur
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Metadaten
Titel
Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software
verfasst von
Hannes Ruelmann
Markus Geveler
Dirk Ribbrock
Peter Zajac
Stefan Turek
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-55874-1_44