GrainLearning

GrainLearning is a Bayesian uncertainty quantification toolbox for computer simulations of granular materials. The software is primarily used to infer model parameter distributions from observation or reference data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process your simulation and observation data, or used as an independent tool where simulations are run separately, e.g., from the command line.

If you use GrainLearning, please cite this paper.
If you want to know more about how the method works, the following papers can be interesting:

• H. Cheng, T. Shuku, K. Thoeni, P. Tempone, S. Luding, V. Magnanimo. An iterative Bayesian filtering framework for fast and automated calibration of DEM models. Comput. Methods Appl. Mech. Eng., 350 (2019), pp. 268-294, 10.1016/j.cma.2019.01.027
• P. Hartmann, H. Cheng, K. Thoeni. Performance study of iterative Bayesian filtering to develop an efficient calibration framework for DEM. Computers and Geotechnics 141, 104491, 10.1016/j.compgeo.2021.104491