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

DOI10.1007/978-3-030-55874-1_44zbMath1471.65203OpenAlexW3158549294MaRDI QIDQ5152837

Dirk Ribbrock, Markus Geveler, P. Zajac, Hannes Ruelmann, Stefan Turek

Publication date: 27 September 2021

Published in: Lecture Notes in Computational Science and Engineering (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2003/38462

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Numerical algorithms for specific classes of architectures (65Y10)

Uses Software

• HONEI
• FEAT3

Cites Work

• Unnamed Item
• HONEI: A collection of libraries for numerical computations targeting multiple processor architectures
• Efficient solvers for incompressible flow problems. An algorithmic and computational approach
• Towards a complete FEM-based simulation toolkit on GPUs: unstructured grid finite element geometric multigrid solvers with strong smoothers based on sparse approximate inverses

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