An iterative sequential Monte Carlo filter for Bayesian calibration of DEM models

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The nonlinear history-dependent macroscopic behavior of granular materials is rooted in the micromechanics at contacts and irreversible rearrangements of the microstructure. This paper presents an iterative sequential Monte Carlo filter to infer micromechanical parameters for DEM modeling of granular materials from macroscopic measurements. To demonstrate the performance of the new Bayesian filter, the stress–strain behavior of fine glass beads under oedometric compression is considered. The parameter sets are initially sampled uniformly in parameter space and then resampled around highly probable subspaces, which shrink towards optimal solutions iteratively. The proposed calibration approach is fast, efficient and automated, because it uses the posterior distribution after a completed iteration as the proposal distribution for the succeeding iteration, and thereby allocating computational power to more probable simulation runs. The Bayesian filter can also serve as a powerful tool for uncertainty quantification and propagation across various scales in multiscale simulation of granular materials.
Original languageEnglish
Title of host publicationNumerical Methods in Geotechnical Engineering IX
Subtitle of host publicationProceedings of the 9th European Conference on Numerical Methods in Geotechnical Engineering (NUMGE 2018)
EditorsManuel de Matos Fernandes
Place of PublicationLondon
PublisherTaylor & Francis
Chapter47
Volume1
Edition1
ISBN (Electronic)9780429446931
DOIs
Publication statusPublished - 22 Jun 2018
Event9th European Conference on Numerical Methods in Geotechnical Engineering, NUMGE 2018 - University of Porto , Porto, Portugal
Duration: 25 Jun 201827 Jun 2018
Conference number: 9

Conference

Conference9th European Conference on Numerical Methods in Geotechnical Engineering, NUMGE 2018
Abbreviated titleNUMGE 2018
CountryPortugal
CityPorto
Period25/06/1827/06/18

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Cheng, H., Luding, S., Magnanimo, V., Shuku, T., Thoeni, K., & Tempone, P. (2018). An iterative sequential Monte Carlo filter for Bayesian calibration of DEM models. In M. D. M. Fernandes (Ed.), Numerical Methods in Geotechnical Engineering IX: Proceedings of the 9th European Conference on Numerical Methods in Geotechnical Engineering (NUMGE 2018) (1 ed., Vol. 1). London: Taylor & Francis. https://doi.org/10.1201/9780429446931-47