The estimation of micromechanical parameters of discrete element method (DEM) models is a nonlinear history-dependent inverse problem. In order to reproduce the experimental measurements with high accuracy, this work aims to develop a machine learning-based calibration toolbox named “Grain learning”, which can extract grains from X-ray computed tomography (CT) images and perform Bayesian parameter estimation for DEM models of dry granular materials.
CITATION STYLE
Cheng, H., Shuku, T., Thoeni, K., Tempone, P., Luding, S., & Magnanimo, V. (2018). Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation. In Springer Series in Geomechanics and Geoengineering (pp. 132–135). Springer Verlag. https://doi.org/10.1007/978-3-319-97112-4_29
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