"In the last years the realistic modelling of nature is becoming a great challenge. More and more details can be modelled since the computing power is rapidly getting higher and more accessible. The presented work tries to make a step into the new type of the so called non-mesh based modelling. The main building block of the modelled material is the random grain. Each grain exists as the separate object bound to its' surrounding, just like it is the case at real polycrystalline materials. The grains are first generated, separately the microstructural properties of the observed material are gathered and the process of grain-shape optimization is used to modify the shape of virtual grains to come as close as possible to the observed real sample. The modeled material is then constructed by the extremely large number of uniquely shaped virtual grains. The representation of a random grain structure is realized using neural networks. The manipulation and optimization of the randomly generated grain shapes is achieved by the genetic algorithm." Prof. Dr. FRANC VODOPIVECÜber den Autor:
A. Bytyqi: obtained PhD degree in Nanoscience and Nanotechnology in 2013. I. Beli?: obtained PhD in Electrical Engineering in 2007. His main research areas are Neural Networks and Modeling. M. Jenko: has worked extensively in the field of Materials Science and Nanoscience. She is the member of different international working groups.
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