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| Titel |
Usage of the Reduced Basis Method and High-Performance Simulations in Geosciences |
| VerfasserIn |
Denise Degen, Karen Veroy, Florian Wellmann |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250144520
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| Publikation (Nr.) |
 EGU/EGU2017-8356.pdf |
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| Zusammenfassung |
| The field of Computational Geosciences often encounters the “curse” of dimensionality, since
it aims at analyzing complex coupled processes over a large domain in space and
time. These high-dimensional problems are computationally intensive, requiring
High-Performance Computing infrastructures. However, constructing parallelized problems
is often not trivial. Therefore, we present a software implementation within the
Multiphysics Object-Orientated Simulation Environment (MOOSE) offering a built-in
parallelization.
Even with the computational potential of High-Performance Computers, it may be
prohibitive to perform model calibrations or inversions for a reasonably large number of
parameters, since the geoscientific forward simulations can be very demanding. Hence, one
desires a method reducing the dimensionality of the problem while retaining the accuracy
within a certain tolerance. Considering model order reduction techniques is a way to achieve
this.
We present the Reduced Basis (RB) Method being such a Model Order Reduction
Technique aiming at considerably reducing the number of degrees of freedom. We show how
the reduction in the dimension results in a significant speed-up, which in turn allows one to
perform sensitivity analyses and parameter estimations, to analyze more complicated
structures, or to obtain results in real-time. In order to demonstrate the powerful
combination of the Reduced Basis Method and High-Performance Computing, we
investigate the method’s of scalability and parallel efficiency, two measurements for the
performance of clusters by using the example of a geothermal conduction problem. |
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