Affiliation:
1. School of Automation, Central South University, Changsha 410083, China
2. School of Computer Science and Engineering, Central South University, Changsha 410083, China
Abstract
Particle locations determine the whole structure of a granular system, which is crucial to understanding various anomalous behaviors in glasses and amorphous solids. How to accurately determine the coordinates of each particle in such materials within a short time has always been a challenge. In this paper, we use an improved graph convolutional neural network to estimate the particle locations in two-dimensional photoelastic granular materials purely from the knowledge of the distances for each particle, which can be estimated in advance via a distance estimation algorithm. The robustness and effectiveness of our model are verified by testing other granular systems with different disorder degrees, as well as systems with different configurations. In this study, we attempt to provide a new route to the structural information of granular systems irrelevant to dimensionality, compositions, or other material properties.
Funder
Natural Science Foundation of Hunan Province
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
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