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Presentation Details

Modelling the neuromuscular system using HPC systems


Thursday, 12 Oct 2017; 11:10 - 11:30 in room 7.12

In Session:
MS03-1: Challenges of Current and Emerging Applications for High Performance Computing (Show complete Mini Symposium)
Thursday, 12 Oct 2017; 10:30 - 12:30 in room 7.12
Show complete Session


1st and presenting Author
Thomas Klotz
Institute of Applied Mechanics (CE) - Group on Continuum Biomechanics and Mechanobiolgy
University of Stuttgart
Stuttgart, Germany
2nd Author
Nehzat Emamy
Institute for Parallel and Distributed Systems
University of Stuttgart
Stuttgart, Germany
3rd Author
Thomas Ertl
Visualisation Research Center of the University of Stuttgart (VISUS)
University of Stuttgart
Stuttgart, Germany
4th Author
Dominik Göddeke
Institute of Applied Analysis and Numerical Simulation
University of Stuttgart
Stuttgart, Germany
5th Author
Aaron Krämer
Institute of Applied Analysis and Numerical Simulation
University of Stuttgart
Stuttgart, Germany
6th Author
Michael Krone
Visualisation Research Center of the University of Stuttgart (VISUS)
University of Stuttgart
Stuttgart, Germany
7th Author
Benjamin Maier
Institute for Parallel and Distributed Systems
University of Stuttgart
Stuttgart, Germany
8th Author
Miriam Mehl
Institute for Parallel and Distributed Systems
University of Stuttgart
Stuttgart, Germany
9th Author
Tobias Rau
Visualisation Research Center of the University of Stuttgart (VISUS)
University of Stuttgart
Stuttgart, Germany
10th Author
Oliver Röhrle
Institute of Applied Mechanics (CE) - Group on Continuum Biomechanics and Mechanobiolgy
University of Stuttgart
Stuttgart, Germany

Micro Abstract:
Modelling the neuromusclular system is challenging due to its high compexlity and variability. Formulating models that account for a realistic biophysical motivated activation process leads to computational expensive multi-scale simulations, which, in turn limits on normal compute environments the model detail and model size due to its computational complexity. We aim to overcome these limitations by using massively parallel HPC clusters.

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