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

Hongyang Cheng, Takayuki Shuku, Klaus Thoeni, Pamela Tempone, Stefan Luding, Vanessa Magnanimo

Research output: Contribution to journalConference article


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.

Original languageEnglish
Pages (from-to)132-135
Number of pages4
JournalSpringer Series in Geomechanics and Geoengineering
Issue number216849
Publication statusPublished - Jan 1 2018
EventChina-Europe Conference on Geotechnical Engineering, 2016 - Vienna, Austria
Duration: Aug 13 2016Aug 16 2016


ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Mechanics of Materials

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