颗粒物质
计算机科学
多尺度建模
复杂系统
粒状材料
领域(数学分析)
中尺度气象学
统计物理学
人工智能
材料科学
物理
数学
气象学
复合材料
化学
计算化学
数学分析
作者
Lia Papadopoulos,Mason A. Porter,Karen E. Daniels,Danielle S. Bassett
出处
期刊:Journal of Complex Networks
[Oxford University Press]
日期:2018-08-01
卷期号:6 (4): 485-565
被引量:100
标识
DOI:10.1093/comnet/cny005
摘要
The arrangements of particles and forces in granular materials have a complex organization on multiple spatial scales that ranges from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate physical, mathematical, statistical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures across different scales in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular matter and explore the potential of such frameworks to provide a useful description of these systems and to enhance understanding of their underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular matter and other kinds of material networks.
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