Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the complex behaviors of such flows that involve solid-like to fluid-like transitions, but they are computationally intractable when simulating large-scale systems. Surrogate models based on statistical or machine learning methods are a viable alternative, but they are typically empirical and rely on a confined set of parameters in evaluating associated risks. Due to their permutation-dependent learning, conventional machine learning models require an unreasonably large amount of training data for building generalizable surrogate models. We employ a graph neural network (GNN), a novel deep learning technique, to develop a GNN-based simulator (GNS) for granular flows to address these issues. Graphs represent the state of granular flows and interactions, like the exchange of energy and momentum between grains, and GNN learns the local interaction law. GNS takes the current state of the granular flow and estimates the next state using Euler explicit integration. We train GNS on a limited set of granular flow trajectories and evaluate its performance in a three-dimensional granular column collapse domain. GNS successfully reproduces the overall behaviors of column collapses with various aspect ratios that were not encountered during training. The computation speed of GNS outperforms high-fidelity numerical simulators by 300 times.

翻译：地质灾害（如滑坡和泥石流）的可靠评估需要对颗粒流动力学进行精确模拟。传统数值方法能够模拟涉及类固体到类流体转变的此类复杂流动行为，但在模拟大规模系统时存在计算瓶颈。基于统计或机器学习方法的替代模型虽具可行性，但往往依赖经验且局限于评估相关风险时的一小套参数。由于对排列顺序具有依赖性，传统机器学习模型需要大量训练数据才能构建泛化性强的替代模型。为解决这些问题，我们采用图神经网络（GNN）这一新型深度学习技术，开发了基于GNN的颗粒流模拟器（GNS）。图结构表征颗粒流状态及颗粒间动量与能量交换等相互作用，GNN则学习局部相互作用规律。GNS通过欧拉显式积分，基于颗粒流当前状态预测下一状态。我们在有限颗粒流运动轨迹上训练GNS，并在三维颗粒柱垮塌场景中评估其性能。GNS成功复现了训练过程中未出现、具有不同高宽比的柱状垮塌整体行为，其计算速度较之高保真数值模拟器提升300倍。