Graph Neural Network-based surrogate model for granular flows

Abstract

Accurate simulation of granular flow dynamics is crucial for assessing geotechnical risks, including landslides and debris flows. Traditional numerical methods are limited by their computational cost in simulating large-scale systems. Statistical or machine learning-based models offer alternatives. Still, they are largely empirical, based on limited 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 (GNN), a state-of-the-art machine learning architecture that learns local interactions. Graphs represent the state of dynamically changing granular flows and their interactions. We implement a multi-Graphics Processing Units (GPU) GNN simulator (GNS) capable of handling different material types. We demonstrate the capability of GNS by modeling granular flow interactions with barriers. GNS takes the granular flow's current state 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 its performance by predicting granular column collapse and interaction with barriers. GNS accurately predicts flow dynamics for column collapses with different aspect ratios and interaction with barriers with configurations unseen during training. GNS is up to a few thousand times faster than high-fidelity numerical simulators.