grainlearning (pypi): A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
Keywords: Bayesian inference|Uncertainty quantification|Non-parametric Gaussian mixture|DEM|Constitutive modeling|Granular materials|bayesian-inference|low-discrepancy-sequences|mixture-models|parameter-identification|sequential-monte-carlo|uncertainty-quantification