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.

使用图神经网络开发了基于 GNN 的 granular flows 模拟器（GNS），成功复现了各种纵横比的柱体崩塌的整体行为，并且计算速度比高保真数值模拟器快 300 倍。

基于图神经网络学习的三维颗粒流动模拟

Three-dimensional granular flow simulation using graph neural  network-based learned simulator

Accurate simulation of granular flow dynamics is crucial for assessing
various geotechnical risks, including landslides and debris flows. Granular
flows involve a dynamic rearrangement of particles exhibiting complex
transitions from solid-like to fluid-like responses. Traditional continuum and
discrete numerical methods are limited by their computational cost in
simulating large-scale systems. Statistical or machine learning-based models
offer an alternative. Still, they are largely empirical, based on a limited set
of parameters. Due to their permutation-dependent learning, traditional machine
learning-based models require huge training data to generalize. To resolve
these problems, we use a graph neural network, a state-of-the-art machine
learning architecture that learns local interactions. Graphs represent the
state of dynamically changing granular flows and the interaction laws, such as
energy and momentum exchange between grains. We develop a graph neural
network-based simulator (GNS) that takes the current state of granular flow and
predicts the next state using Euler explicit integration by learning the local
interaction laws. We train GNS on different granular trajectories. We then
assess the performance of GNS by predicting granular column collapse. GNS
accurately predicts flow dynamics for column collapses with different aspect
ratios unseen during training. GNS is hundreds of times faster than
high-fidelity numerical simulators. The model also generalizes to domains much
larger than the training data, handling more than twice the number of particles
than it was trained on.

该研究使用图神经网络建立了一种基于局部交互法则的颗粒流动的高效模拟器，称为 GNS。该模型在训练中考虑了不同颗粒轨迹，并且在测试中展现了优秀的性能，可用于预测不同宽高比下的颗粒柱倒塌情况。GNS 快速且可泛化到远大于训练数据的粒子数目范畴。