  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.
