Data-driven selection of tessellation models describing polycrystalline microstructures

DOI10.1007/s10955-018-2096-8zbMath1405.82034OpenAlexW2810801284MaRDI QIDQ1990102

Yanyan Li

Publication date: 24 October 2018

Published in: Journal of Statistical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10955-018-2096-8

zbMATH Keywords

tessellationmodel selectionpolycrystalline materialAkaike information criterionBayesian information criterionstructural risk minimization

Mathematics Subject Classification ID

Geometric probability and stochastic geometry (60D05) Statistical mechanics of crystals (82D25) Combinatorial aspects of tessellation and tiling problems (05B45) Statistical ranking and selection procedures (62F07) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

Exploration of Gibbs-Laguerre tessellations for three-dimensional stochastic modeling

Uses Software

• DREAM.3D

Cites Work

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• Estimating the dimension of a model
• Voronoi Diagrams and Delaunay Triangulations
• DESCRIPTION OF THE 3D MORPHOLOGY OF GRAIN BOUNDARIES IN ALUMINUM ALLOYS USING TESSELLATION MODELS GENERATED BY ELLIPSOIDS
• Handbook of Spatial Statistics
• Anisotropic Growth of Voronoi Cells
• Power Diagrams: Properties, Algorithms and Applications
• Random Tessellations and Boolean Random Functions

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