Bayesian Calibration of DEM models via sequential Monte Carlo filtering
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Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter
During my PhD, I initiated my own research on Bayesian calibration/uncertainty quantification for DEM simulations, in collaboration with Dr Takayuki Shuku at Okayama University and Dr. Klaus Thoeni at the University of Newcastle. We introduced the concept of Bayesian calibration to geotechnics and powder technology. The Bayesian calibration tool fills the longstanding gap between macro-experiments and micro-simulations. Using advanced machine learning, I further boosted the efficiency of the Bayesian framework and implemented it as a distinguished feature of the open-source DEM code YADE. The method applies to not only micro-models, e.g., glass beads and sand, but also macroscopic constitutive laws.
Comparison of triaxial responses obtained in the experiments and DEM simulations using the classical linear (CL) and Hertz-Mindlin contact (HM) laws
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