Compressing Random Microstructures via Stochastic Wang Tilings

Abstract

This paper presents a stochastic Wang tiling based technique to compress or reconstruct disordered microstructures on the basis of given spatial statistics. Unlike the existing approaches based on a single unit cell, it utilizes a finite set of tiles assembled by a stochastic tiling algorithm, thereby allowing to accurately reproduce long-range orientation orders in a computationally efficient manner. Although the basic features of the method are demonstrated for a two-dimensional particulate suspension, the present framework is fully extensible to generic multi-dimensional media.

4 pages, 6 figures, v2: minor changes as suggested by reviewers, v3: corrected two typos in the revised version

References in corpus (4)

• A Superior Descriptor of Random Textures and Its Predictive Capacity
• Entropic descriptor of a complex behaviour
• Microstructural enrichment functions based on stochastic Wang tilings
• First Order Phase Transition in a Model of Quasicrystals

Cited by in corpus (10)

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• Microstructural enrichment functions based on stochastic Wang tilings
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• Wang tiling aided statistical determination of the Representative Volume Element size of random heterogeneous materials
• Modular-topology optimization with Wang tilings: An application to truss structures
• Level-set based design of Wang tiles for modelling complex microstructures
• On bounded Wang tilings
• Compression and Reconstruction of Random Microstructures using Accelerated Lineal Path Function
• Microstructure-informed reduced modes synthesized with Wang tiles and the Generalized Finite Element Method