A neural accelerated roughness-resolved Discrete Element Method (Rough-DEM) for modeling granular systems
Abstract
The Discrete Element Method (DEM) is an indispensable tool for modeling and understanding the basic mechanical behaviors of (dry) granular media, a class of disordered material systems of significant interest to multiple scientific domains from (geo-)physics to (chemical) engineering. At the core of all present DEM frameworks is a phenomenological friction law--commonly chosen to be of the Amontons-Coulomb type--that models the frictional interaction among constituent grains. Here, inspired by research in the tribology community, we present an alternative, fundamentally different framework, termed roughness-resolved DEM (Rough-DEM), in which contact friction is handled in a more physics-based manner. In this framework, instead of using a friction law, we explicitly resolve the surface topography of each grain and let friction emerge from collective frictionless interactions of surface asperities. In essence, we trade phenomenology for physics at the cost of an increased computational demand. To mitigate the cost, we introduce an FE2-like algorithm and a feature-wise linear modulation (FiLM)-based neural network surrogate, both of which are parallelized on GPU to accelerate contact computation. Crucially, using this framework in 2D, we (1) demonstrate the possibility of recovering a two-body-based Amontons-Coulomb-type friction behavior using a \textit{simple} representation of surface topography, and (2) identify many-body-based frictional behaviors--more specifically, the angle of repose values--that \textit{cannot always} be captured by current DEM frameworks under the same level of model complexity. We conclude by discussing future directions for Rough-DEM, as well as exciting new opportunities it could unlock for a wide range of emerging applications, including (designing) suspension rheology and metamaterial mechanics.
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BibTeX
@misc{pan2026neural,
title = {{A neural accelerated roughness-resolved Discrete Element Method (Rough-DEM) for modeling granular systems}},
author = {Xiwei Pan and Liuchi Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33363},
eprint = {2610.33363},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33363},
}
RIS
TY - UNPB
TI - A neural accelerated roughness-resolved Discrete Element Method (Rough-DEM) for modeling granular systems
AU - Xiwei Pan
AU - Liuchi Li
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.33363
UR - https://arxiv.org/abs/2610.33363
AB - The Discrete Element Method (DEM) is an indispensable tool for modeling and understanding the basic mechanical behaviors of (dry) granular media, a class of disordered material systems of significant interest to multiple scientific domains from (geo-)physics to (chemical) engineering. At the core of all present DEM frameworks is a phenomenological friction law--commonly chosen to be of the Amontons-Coulomb type--that models the frictional interaction among constituent grains. Here, inspired by research in the tribology community, we present an alternative, fundamentally different framework, termed roughness-resolved DEM (Rough-DEM), in which contact friction is handled in a more physics-based manner. In this framework, instead of using a friction law, we explicitly resolve the surface topography of each grain and let friction emerge from collective frictionless interactions of surface asperities. In essence, we trade phenomenology for physics at the cost of an increased computational demand. To mitigate the cost, we introduce an FE^2-like algorithm and a feature-wise linear modulation (FiLM)-based neural network surrogate, both of which are parallelized on GPU to accelerate contact computation. Crucially, using this framework in 2D, we (1) demonstrate the possibility of recovering a two-body-based Amontons-Coulomb-type friction behavior using a \textit{simple} representation of surface topography, and (2) identify many-body-based frictional behaviors--more specifically, the angle of repose values--that \textit{cannot always} be captured by current DEM frameworks under the same level of model complexity. We conclude by discussing future directions for Rough-DEM, as well as exciting new opportunities it could unlock for a wide range of emerging applications, including (designing) suspension rheology and metamaterial mechanics.
ER -