  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.
