GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials

Python PureBasic Jupyter Notebook Submitted 12 January 2024 • Published 02 May 2024
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Editor: @diehlpk (all papers)
Reviewers: @gchure (all reviews), @Haipeng-ustc (all reviews)

Authors

Hongyang Cheng (0000-0001-7652-8600), Luisa Orozco (0000-0002-9153-650X), Retief Lubbe, Aron Jansen (0000-0002-4764-9347), Philipp Hartmann (0000-0002-2524-8024), Klaus Thoeni (0000-0001-7351-7447)

Citation

Cheng et al., (2024). GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials. Journal of Open Source Software, 9(97), 6338, https://doi.org/10.21105/joss.06338

@article{Cheng2024, doi = {10.21105/joss.06338}, url = {https://doi.org/10.21105/joss.06338}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {97}, pages = {6338}, author = {Hongyang Cheng and Luisa Orozco and Retief Lubbe and Aron Jansen and Philipp Hartmann and Klaus Thoeni}, title = {GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials}, journal = {Journal of Open Source Software} }
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Bayesian inference Calibration Discrete element method Granular materials Uncertainty Quantification Multi-particle simulation

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Journal of Open Source Software is an affiliate of the Open Source Initiative.

Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.

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ISSN 2475-9066