Bayesian propagation between micro and macro
Bayesian uncertainty quantification for geomechanical models at micro and macro scales
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Uncertainty exists in geomaterials at contact, microstructural and continuum scales. To develop predictive, robust multi-scale models for geotechnical problems, the new challenge is to allow for the propagation of model/parameter uncertainty (conditioned on laboratory/field measurements) between micro and macro scales. We aim to first quantify these uncertainties using an iterative Bayesian filtering framework. In this contribution, we first respectively quantify the parameter uncertainty of models that are discrete and continuum in nature, namely a discrete particle and an elasto-plastic model.
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Correlations and NorSand parameters resampled at various iterations of Bayesian filtering.
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Correlations between NorSand (macro) and Hertz-Mindlin (macro) parameters and their uncertainty (error bars)
