Machine learning approaches for repositories of numerical simulation results

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.