EIKONAL-BASED MODELS OF RANDOM TESSELLATIONS

In this article, we propose a novel, efficient method for computing a random tessellation from its vectorial representation at each voxel of a discretized domain. This method is based upon the resolution of the Eikonal equation and has a complexity in O(N log N), N being the number of voxels used to discretize the domain. By contrast, evaluating the implicit functions of the vectorial representation at each voxel location has a complexity of O(N²) in the general case. The method also enables us to consider the generation of tessellations with rough interfaces between cells by simulating the growth of the germs on a domain where the velocity varies locally. This aspect constitutes the main contribution of the article. A final contribution is the development of an algorithm for estimating the multi-scale tortuosity of the boundaries of the tessellation cells. The algorithm computes the tortuosity of the boundary at several scales by iteratively deforming the boundary until it becomes a straight line. Using this algorithm, we demonstrate that depending on the local velocity model, it is possible to control the roughness amplitude of the cells boundaries.

Developed Random Tessellation for Modeling of Microstructure

The concept of random tessellation is extensively used in wide area of natural sciences, especially material sciences. In this paper a simple but complete explanation of the random tessellation and mathematical tools requirements is presented. Then introducing the algorithm and the program for display random tessellation diagram was written. This program, with high speed and simple algorithm for random tessellation has the ability to change the level of statistical parameters such as number, mean, variance of the area of the grain. The ability to model microstructures of metals and grains for mechanical application, such as estimation of mechanical properties and crack propagation model in microstructure scale is very important. Finally, the microstructure produced by this program show good fitness of random generation with real microstructure.

New Classes of Random Tessellations Arising from Iterative Division of Cells

We present new ideas about the type of random tessellation which evolves through successive division of its cells. These ideas are developed in an intuitive way, with many pictures and only a modicum of mathematical formalism–so that the wide application of the ideas is clearly apparent to all readers. A vast number of new tessellation models, with known probability distribution for the volume of the typical cell, follow from the concepts in this paper. There are other interesting models for which results are not presented (or presented only through simulation methods), but these models have illustrative value. A large agenda of further research is opened up by the ideas in this paper.

New Classes of Random Tessellations Arising from Iterative Division of Cells

We present new ideas about the type of random tessellation which evolves through successive division of its cells. These ideas are developed in an intuitive way, with many pictures and only a modicum of mathematical formalism–so that the wide application of the ideas is clearly apparent to all readers. A vast number of new tessellation models, with known probability distribution for the volume of the typical cell, follow from the concepts in this paper. There are other interesting models for which results are not presented (or presented only through simulation methods), but these models have illustrative value. A large agenda of further research is opened up by the ideas in this paper.

The iteration of random tessellations and a construction of a homogeneous process of cell divisions

A random tessellation of ℝd is said to be homogeneous if its distribution is invariant under all shifts of ℝd. The iteration of homogeneous random tessellations is described in a new manner that makes it evident that the resulting random tessellation is homogeneous again. Furthermore, a tessellation-valued process is constructed, the random states of which are homogeneous random tessellations stable under iteration (STIT). It can be interpreted as a process of subsequent division of cells.

The iteration of random tessellations and a construction of a homogeneous process of cell divisions

A random tessellation of ℝ d is said to be homogeneous if its distribution is invariant under all shifts of ℝ d . The iteration of homogeneous random tessellations is described in a new manner that makes it evident that the resulting random tessellation is homogeneous again. Furthermore, a tessellation-valued process is constructed, the random states of which are homogeneous random tessellations stable under iteration (STIT). It can be interpreted as a process of subsequent division of cells.