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Journal Article

Bio-numerical simualtions with SimBio

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Wolters,  Carsten Hermann
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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mpi_703.pdf
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Citation

Fingberg, J., Berti, G., Hartmann, H., Basermann, A., Wolters, C. H., Anwander, A., et al. (2003). Bio-numerical simualtions with SimBio. NEC Research and Development, 44(1), 140-145.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-A196-8
Abstract
The central objective of the SimBio§ project is the improvement of clinical and medical practices by the use of large-scale numerical simulation for bio-medical problems. SimBio provides a generic simulation environment running on parallel and distributed computing systems. An innovative key feature is the input of patient specific data to the modelling and simulation process. While future SimBio users will be able to develop application specific tools to improve practices in many areas, the project evaluation & validation will demonstrate improvements in: non-invasive diagnosis and pre-operative planning and the design of prostheses. The SimBio environment consists of components for the discrete representation of the physical problem, the numerical solution system, inverse problem solving, optimization and visualization. The core of the environment is the numerical solution system comprising parallel Finite Element solvers and advanced numerical library routines. The compute-intensive components are implemented on high performance comput- ing (HPC) platforms. The following article explains the HPC requirements of the bio-medical project applica- tions and presents the SimBio solutions for the project validation examples: electromagnetic source localization within the human brain, bio-mechanical simulations of the human head and the design of knee joint menisci replacements. Results include performance measurements of the parallel solvers in the SimBio environment. The paper concludes with an outlook on future Grid-computing activities based on SimBio developments.