2019
DOI: 10.5566/ias.2061
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Eikonal-Based Models of Random Tessellations

Abstract: 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… Show more

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Cited by 7 publications
(4 citation statements)
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References 18 publications
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“…For each nucleus, a cell propagates in 3D through the aggregate. The cell growth is simulated using the Eikonal equation [26]. When two cells meet, they stop growing and their contact surface is transformed into a volume filled with PEEK with a randomly generated thickness sampled from an exponential law.…”
Section: D Numerical Simulation Of the Microstructurementioning
confidence: 99%
“…For each nucleus, a cell propagates in 3D through the aggregate. The cell growth is simulated using the Eikonal equation [26]. When two cells meet, they stop growing and their contact surface is transformed into a volume filled with PEEK with a randomly generated thickness sampled from an exponential law.…”
Section: D Numerical Simulation Of the Microstructurementioning
confidence: 99%
“…In addition, the annotation itself is error prone due to the inevitable loss of attention of the operator in charge of annotating the images. The main originality of our approach is that we entirely trained the network on a series of synthetic images generated with morphological models (Figliuzzi, 2019;Figliuzzi et al, 2021;Jeulin, 2021;Stoyan et al, 2013), commonly used to simulate micro-structures in materials engineering (Bortolussi et al, 2018;Figliuzzi et al, 2016), rather than on images of real experiments. With our approach of artificial synthesis of the ground truth, the positions and sizes of the particles are known by construction, which allows us to get rid of the difficulty of obtaining a reliable learning database 1 .…”
Section: Introductionmentioning
confidence: 99%
“…Materials used in industry often present a complex internal microstructure, which largely determines most of their physical properties at the macroscopic level (Jeulin, 1991;Ohser, 2009;Torquato, 2013;Moussaoui, 2018;2019;Figliuzzi, 2019). A common way to carry out a quantitative study of the microstructure influence on the macroscopic properties of materials is to generate random microstructures that reproduce their geometrical characteristics (Moreaud, 2012;Wang, 2015).…”
Section: Introductionmentioning
confidence: 99%