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Contact

TU Dortmund
Faculty of Mathematics — Chair III of Applied Mathematics
Vogelpothsweg 87
44227 Dortmund
Germany
Phone: +49-(0)231-755-3076
Fax: +49-(0)231-755-5933
E-mail:

M. Sc. Hannes Ruelmann

Technische Universität Dortmund
Fakultät für Mathematik
Lehrstuhl LSIII
Vogelpothsweg 87
44227 Dortmund
M. Sc. Hannes Ruelmann
RoomM 1036
Telephone(0231) 755-3134
Telefax(0231) 755-5933
E-mail

Curriculum Vitae

06/2009-06/2011 Training as an Industrial Business Management Assistant
10/2011-11/2014 B.Sc. Study of Technomathematics, with special emphasis to Applied mathematics and Numerical analysis and as minor subject mechanical engineering
11/2014-09/2017 M.Sc. Study of Technomathematics, with special emphasis to Applied mathematics and Numerical analysis and as minor subject mechanical engineering
since 2017 Ph.D. study

Tasks

  • Wissenschalftlicher Mitarbeiter (Forschung)
  • Programmierkurs
  • Computerorientiertes Problemlösen
  • Studienprojekte
  • Einführung in die Technomathematik
  • Simulationstechniken
  • Vizepräsident des 'LS3 coffee club'

Research Interests

  • Finite Elemente Methode
  • Machine Learning

Papers

Authors Title / Event / Year Download
Ruelmann, H.; Geveler, M.; Ribbrock, D.; Zajac, P.; Turek, S. Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software, Vermolen, F., Vuik, C., Lecture Notes in Computational Science and Engineering, 139, 449-457, Numerical Mathematics and Advanced Applications Enumath 2019, Springer, 2020 [BibTeX]
Ruelmann, H.; Geveler, M.; Ribbrock, D.; Zajac, P.; Turek, S. Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software, Ergebnisberichte des Instituts für Angewandte Mathematik Nummer 618, Fakultät für Mathematik, TU Dortmund, 618, 2019 [BibTeX] [PDF]
Ruelmann, H.; Geveler, M.; Turek, S. On the prospects of using machine learning for the numerical simulation of PDEs: Training neural networks to assemble approximate inverses, Eccomas Newsletter, 27-32, 2018 [BibTeX]
Ruelmann, H.; Geveler, M.; Turek, S. On the Prospects of Using Machine Learning for the Numerical Simulation of PDEs: Training Neural Networks to Assemble Approximate Inverses, Ergebnisberichte des Instituts für Angewandte Mathematik Nummer 581, Fakultät für Mathematik, TU Dortmund, 581, 2018 [BibTeX] [PDF]
Geveler, M.; Ribbrock, D.; Donner, D.; Ruelmann, H.; Höppke, C.; Schneider, D.; Tomaschewski, D.; Turek, S. The ICARUS white paper: A scalable, energy-efficient, solar-powered HPC center based on low power GPUs, Ergebnisberichte des Instituts für Angewandte Mathematik Nummer 565, Fakultät für Mathematik, TU Dortmund, 565, 2017 [BibTeX] [PDF]

Talks

Authors Title / Event / Year Download
Ruelmann, H.; Turek, S. Basic Machine Learning Approaches for the Acceleration of PDE Simulations, Computational Science and AI in Industry (CSAI 2021) ECCOMAS Thematic Conference, Virtual Conference, Juni 2021 [BibTeX] [PDF]
Ruelmann, H.; Geveler, M.; Turek, S. Basic Machine Learning Approaches for the Acceleration of PDE Simulations, European Numerical Mathematics and Advanced Applications Conference (ENUMATH), Egmond aan Zee, The Netherlands, Oktober 2019 [BibTeX]
Geveler, M.; Ruelmann, H.; Turek, S. Machine Learning Approaches for the Acceleration of the Linear Solver in PDE Simulations, , April 2019, Workshop on Machine Learning Algorithms in Scientific Computing, Nottingham [BibTeX] [PDF]
Ruelmann, H.; Geveler, M.; Turek, S. On the prospects of using machine learning for the numerical simulation of PDEs: Training neural networks to assemble approximate inverses, , Juni 2018 [Abstract] [BibTeX]
Geveler, M.; Ribbrock, D.; Ruelmann, H.; Donner, D.; Höppke, C.; Schneider, D.; Tomaschewski, D.; Turek, S. The ICARUS white paper: A scalable, energy-efficient, solar-powered HPC center based on low power GPUs, UcHPC'16 at Euro-Par'16, Grenoble, Aug 2016 [BibTeX] [PDF]