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Machine learning approaches for repositories of numerical simulation results

: Garcke, J.; Iza Teran, R.

Volltext (PDF; )

DYNAmore GmbH, Stuttgart:
10th European LS-DYNA Conference 2015. Proceedings CD-ROM : 15 - 17 June 2015, Würzburg, Germany
Stuttgart: DYNAmore, 2015
ISBN: 978-3-9816215-2-5
10 S.
European LS-DYNA Conference <10, 2015, Würzburg>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer SCAI ()

Simulations are used intensively in the developing process of new industrial products and have achieved a high degree of detail. In that workflow often up to thousand finite element model variants, representing different product configurations, are simulated within a few days. Currently the decision process for finding the optimal product parameters involves the comparative evaluation of large finite element simulation bundles by post-processing each one of those results using 3D visualization software. This time consuming process creates a severe bottleneck in the product design and evaluation workflow.