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Data-driven multiscale modelling of granular materials via knowledge transfer and sharing

Tongming Qu Orcid Logo, Jidong Zhao, Shaoheng Guan Orcid Logo, Yuntian Feng Orcid Logo

International Journal of Plasticity, Volume: 171, Start page: 103786

Swansea University Author: Yuntian Feng Orcid Logo

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DOI (Published version): 10.1016/j.ijplas.2023.103786

Abstract

Machine learning approaches have found immense potential to revolutionise the constitutive modelling of granular materials. However, data scarcity poses a significant challenge to this emerging paradigm. This study aims to tackle this issue by presenting two transfer learning-based strategies that h...

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Published in: International Journal of Plasticity
ISSN: 0749-6419
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64899
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Abstract: Machine learning approaches have found immense potential to revolutionise the constitutive modelling of granular materials. However, data scarcity poses a significant challenge to this emerging paradigm. This study aims to tackle this issue by presenting two transfer learning-based strategies that harness well-established constitutive knowledge and similar material data to reduce data demands for data-driven material modelling. The first approach utilises phenomenological constitutive models to generate massive synthetic data which reflect the targeted material behaviour to train a base model. This base model is then repurposed for a new task based on numerical simulation data via transfer learning. The other approach involves using available material data to train a base model, which is then applied to other new materials that are similar but with limited data. The proposed transfer learning methods are tested on both particle-scale simulations of representative volume elements (RVEs) and hierarchical multiscale modelling of boundary value problems (BVPs) of granular materials. The trained data-driven material model is embedded in numerical simulations with the finite element method (FEM) to validate its accuracy, efficiency, and stability. The results demonstrate that transfer learning can effectively achieve high-quality machine learning predictions with limited data. The transfer learning strategy presented in this study is expected to be widely applicable to small data-driven material modelling.
Keywords: Granular materials; DEM; Machine learning; Transfer learning; Data-driven material modelling; Hierarchical multiscale modelling
College: Faculty of Science and Engineering
Funders: The study was financially supported by the National Natural Science Foundation of China (via General Project #11972030) and the Research Grants Council of Hong Kong (under GRF #16208720).
Start Page: 103786

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