# Graph Neural Network-based surrogate model for granular flows

arXiv: https://arxiv.org/abs/2305.05218 · HTML: https://mlsanity.com/paper/2305.05218/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Learning to Simulate Complex Physics with Graph Networks](https://mlsanity.com/paper/2002.09405/) (2020)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Constraint-based graph network simulator](https://mlsanity.com/paper/2112.09161/) (2021)
- [Differentiable Physics-informed Graph Networks](https://mlsanity.com/paper/1902.02950/) (2019)
- [GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling](https://mlsanity.com/paper/2211.10228/) (2022)
- [Scalable and modular material point method for large-scale simulations](https://mlsanity.com/paper/1909.13380/) (2019)
- [Learning to Simulate Unseen Physical Systems with Graph Neural Networks](https://mlsanity.com/paper/2201.11976/) (2022)

## Built on by (most cited first, 16 of 16)

- [Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems](https://mlsanity.com/paper/2309.13348/) (2023)
- [Inverse analysis of granular flows using differentiable graph neural network simulator](https://mlsanity.com/paper/2401.13695/) (2024)
- [JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling](https://mlsanity.com/paper/2507.04192/) (2025)
- [Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems](https://mlsanity.com/paper/2501.07373/) (2025)
- [Machine Learning Aided Modeling of Granular Materials: A Review](https://mlsanity.com/paper/2410.14767/) (2024)
- [Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators](https://mlsanity.com/paper/2511.05456/) (2025)
- [Three-dimensional granular flow simulation using graph neural network-based learned simulator](https://mlsanity.com/paper/2311.07416/) (2023)
- [Development of a graph neural network surrogate for travel demand modelling](https://mlsanity.com/paper/2408.07726/) (2024)
- [Towards scientific machine learning for granular material simulations -- challenges and opportunities](https://mlsanity.com/paper/2504.08766/) (2025)
- [MLPs and KANs for data-driven learning in physical problems: A performance comparison](https://mlsanity.com/paper/2504.11397/) (2025)
- [Emergence of Computational Structure in a Neural Network Physics Simulator](https://mlsanity.com/paper/2504.11830/) (2025)
- [Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems](https://mlsanity.com/paper/2504.13768/) (2025)
- [Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics](https://mlsanity.com/paper/2510.25683/) (2025)
- [A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering](https://mlsanity.com/paper/2512.24365/) (2025)
- [Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids](https://mlsanity.com/paper/2606.23251/) (2026)
- [TRACE: A spatiotemporal contact memory graph network simulator for granular dynamics](https://mlsanity.com/paper/2609.02991/) (2026)

## Abstract (arXiv)

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.