Towards Scientific Machine Learning for Granular Material Simulations : Challenges and Opportunities
(2025) In Archives of Computational Methods in Engineering 2025.- Abstract
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged... (More)
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.
(Less)
- author
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Archives of Computational Methods in Engineering
- volume
- 2025
- pages
- 33 pages
- publisher
- Springer
- external identifiers
-
- scopus:105013642739
- ISSN
- 1134-3060
- DOI
- 10.1007/s11831-025-10322-8
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2025.
- id
- 928f3a8a-715f-48a3-9ec9-a2d3a7aec1f6
- date added to LUP
- 2025-08-28 13:08:50
- date last changed
- 2025-12-18 16:08:08
@article{928f3a8a-715f-48a3-9ec9-a2d3a7aec1f6,
abstract = {{<p>Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.</p>}},
author = {{Fransen, Marc and Fürst, Andreas and Tunuguntla, Deepak and Wilke, Daniel N. and Alkin, Benedikt and Barreto, Daniel and Brandstetter, Johannes and Cabrera, Miguel Angel and Fan, Xinyan and Guo, Mengwu and Kieskamp, Bram and Kumar, Krishna and Morrissey, John and Nuttall, Jonathan and Ooi, Jin and Orozco, Luisa and Papanicolopulos, Stefanos Aldo and Qu, Tongming and Schott, Dingena and Shuku, Takayuki and Sun, Wai Ching and Weinhart, Thomas and Ye, Dongwei and Cheng, Hongyang}},
issn = {{1134-3060}},
language = {{eng}},
publisher = {{Springer}},
series = {{Archives of Computational Methods in Engineering}},
title = {{Towards Scientific Machine Learning for Granular Material Simulations : Challenges and Opportunities}},
url = {{http://dx.doi.org/10.1007/s11831-025-10322-8}},
doi = {{10.1007/s11831-025-10322-8}},
volume = {{2025}},
year = {{2025}},
}