Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation

Title: Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation
Creator: Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Tempone, Pamela; Luding, Stefan; Magnanimo, Vanessa
Relation: China-Europe Conference on Geotechnical Engineering. Proceedings of China-Europe Conference on Geotechnical Engineering [presented in Springer Series in Geomechanics and Geoengineering, Vol. 1] (Vienna, Austria 13-16 August, 2016) p. 132-135
Publisher Link: http://dx.doi.org/10.1007/978-3-319-97112-4_29
Publisher: Springer
Resource Type: conference paper
Date: 2018
Description: 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.
Subject: discrete element method; grain learning; x-ray computed tomography images; Bayesian parameter estimation
Identifier: http://hdl.handle.net/1959.13/1405017
Identifier: uon:35426
Identifier: ISBN:9783319971124
Language: eng
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