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arXiv · 2204.06430

Dynamic grain models via fast heuristics for diagram representations

Andreas Alpers·Maximilian Fiedler·Peter Gritzmann·Fabian Klemm
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Abstract

The present paper introduces a mathematical model for studying dynamic grain growth. In particular, we show how characteristic measurements, grain volumes, centroids, and central second-order moments at discrete moments in time can be turned quickly into a continuous description of the grain growth process in terms of geometric diagrams (which largely generalize the well-known Voronoi and Laguerre tessellations). We evaluate the computational behavior of our algorithm on real-world data.

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Andreas Alpers, Maximilian Fiedler, Peter Gritzmann, Fabian Klemm. 2022-04-13. Dynamic grain models via fast heuristics for diagram representations. https://doi.org/10.1080/14786435.2023.2180679

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