We report on the development of a recurrent neural network (RNN) that models the density relaxation process in initially disordered assemblies of monodisperse spheres within a tapped, three-dimensional container. The RNN model is trained on coordinate data sets generated from granular dynamics simulations to examine microstructure development. In particular, the physics-based model is designed to simulate the evolution of bulk density (characterized by the average solids fraction) within a laterally periodic computational volume starting from an initial, random arrangement of its spheres. Drastically different progressions of individual realizations were observed, often commensurate with the sporadic occurrence of pronounced jumps in density over the duration of a small number of taps. This behavior is consistent with a collective reorganization process previously reported in the literature as an inherent physical feature of the density relaxation process. Visualizations further reveal the formation of crystalline regions separated by dislocations that facilitate bulk sliding motion in the system. To understand how initial conditions and system parameters influence this phenomenon, a considerable amount of data is needed. However, the physics-based simulations necessary to collect this data are too computationally demanding and time consuming. To address this shortcoming and provide a platform for future work, we develop a surrogate RNN model and assess its fidelity with the original physics-based model. Our results suggest that such a surrogate model has the potential to be an important tool in granular systems modeling and research.

我们报告了循环神经网络 (RNN) 的开发，该网络对轻敲三维容器内单分散球体最初无序组装的密度弛豫过程进行建模。RNN 模型在颗粒动力学模拟生成的坐标数据集上进行训练，以检查微观结构的发展。特别是，基于物理的模型旨在模拟横向周期性计算体积内堆积密度（以平均固体分数为特征）的演变，从球体的初始随机排列开始。观察到个体实现的截然不同的进展，通常与在少量敲击的持续时间内零星发生的明显的密度跳跃相称。这种行为与文献中先前报道的作为密度弛豫过程的固有物理特征的集体重组过程一致。可视化进一步揭示了由位错分隔的晶体区域的形成，这些位错促进了系统中的整体滑动运动。为了了解初始条件和系统参数如何影响这种现象，需要大量数据。然而，收集这些数据所需的基于物理的模拟计算量要求过高且耗时。为了解决这个缺点并为未来的工作提供一个平台，我们开发了一个替代 RNN 模型，并评估其与原始基于物理的模型的保真度。我们的结果表明，这种替代模型有可能成为粒状系统建模和研究的重要工具。