变形(气象学)
计算机科学
材料科学
生物系统
人工神经网络
图形
统计物理学
人工智能
理论计算机科学
物理
复合材料
生物
作者
Rituparno Mandal,Corneel Casert,Peter Sollich
标识
DOI:10.1038/s41467-022-31732-3
摘要
Abstract Force chains are quasi-linear self-organised structures carrying large stresses and are ubiquitous in jammed amorphous materials like granular materials, foams or even cell assemblies. Predicting where they will form upon deformation is crucial to describe the properties of such materials, but remains an open question. Here we demonstrate that graph neural networks (GNN) can accurately predict the location of force chains in both frictionless and frictional materials from the undeformed structure, without any additional information. The GNN prediction accuracy also proves to be robust to changes in packing fraction, mixture composition, amount of deformation, friction coefficient, system size, and the form of the interaction potential. By analysing the structure of the force chains, we identify the key features that affect prediction accuracy. Our results and methodology will be of interest for granular matter and disordered systems, e.g. in cases where direct force chain visualisation or force measurements are impossible.
科研通智能强力驱动
Strongly Powered by AbleSci AI