Zhang Hang, Li Xingqiao, Li Zirui, Huang Duan, Zhang Ling
School of Automation, Central South University, Changsha 410083, China.
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Micromachines (Basel). 2023 Mar 23;14(4):714. doi: 10.3390/mi14040714.
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.
颗粒位置决定了颗粒系统的整体结构，这对于理解玻璃和非晶态固体中的各种异常行为至关重要。如何在短时间内准确确定此类材料中每个颗粒的坐标一直是一个挑战。在本文中，我们使用一种改进的图卷积神经网络，仅根据每个颗粒的距离信息来估计二维光弹性颗粒材料中的颗粒位置，这些距离可以通过距离估计算法预先估计。我们的模型通过对具有不同无序度的其他颗粒系统以及不同构型的系统进行测试，验证了其稳健性和有效性。在本研究中，我们试图提供一条获取与维度、组成或其他材料属性无关的颗粒系统结构信息的新途径。
