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.
@article{arxiv.2204.06430,
title = {Dynamic grain models via fast heuristics for diagram representations},
author = {Andreas Alpers and Maximilian Fiedler and Peter Gritzmann and Fabian Klemm},
journal= {arXiv preprint arXiv:2204.06430},
year = {2023}
}