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Published February 2022 | public
Journal Article

Bridging length scales in granular materials using convolutional neural networks


Granular materials are complex systems whose macroscopic mechanics are governed by particles at the grain-scale. The need to understand their grain-scale behavior has motivated significant experimental and modeling efforts. Bridging the grain-scale with the continuum scale is important in order to develop constitutive theories based on grain-scale behavior, as well as for interpreting the results of grain-scale models and experiments from a macroscopic context. In this work, we present a new data-driven framework based on convolutional neural networks to bridge the grain-scale and continuum scale in granular materials. We use this framework to obtain a micromechanical model of stress and demonstrate that spatial correlations at the grain-scale are critical for bridging length scales. Our results suggest that it is possible to learn data-driven relationships between the grain-scale and macroscale even if we have limited knowledge about the physical state of a granular system. We also observed that it is possible to train a model to predict macroscopic stress using only a subset of the contact data for each time step. This points to the discovery of a new pattern in granular systems, whereby any spatially correlated subset of contact data is sufficient to model macroscopic stress, regardless of how much force they may be carrying. Finally, we demonstrated that our framework is robust with potential for generalizability in time.

Additional Information

© OWZ 2021. Received 17 December 2020; Revised 11 March 2021; Accepted 17 March 2021; Published 01 April 2021. Data Availability: Data were obtained from the work done by Marteau and Andrade [42]. The authors acknowledge funding support by Army Research Office, under Grant Number W911NF-17-1-0212. The authors declare that they have no conflict of interest.

Additional details

August 22, 2023
October 23, 2023