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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

62 Downloads (Pure)

Original language	English
Article number	6338
Number of pages	4
Journal	Journal of open source software
Volume	9
Issue number	97
DOIs	https://doi.org/10.21105/joss.06338
Publication status	Published - 2 May 2024

Access to Document

@article{c327cd96bb5b426eaa66f2c1428778ac,

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

author = "Hongyang Cheng and Luisa Orozco and Retief Lubbe and Aron Jansen and Philipp Hartmann and Klaus Thoeni",

year = "2024",

month = may,

day = "2",

doi = "10.21105/joss.06338",

language = "English",

volume = "9",

journal = "Journal of open source software",

issn = "2475-9066",

number = "97",

}

TY - JOUR

T1 - GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials

AU - Cheng, Hongyang

AU - Orozco, Luisa

AU - Lubbe, Retief

AU - Jansen, Aron

AU - Hartmann, Philipp

AU - Thoeni, Klaus

PY - 2024/5/2

Y1 - 2024/5/2

U2 - 10.21105/joss.06338

DO - 10.21105/joss.06338

M3 - Article

SN - 2475-9066

VL - 9

JO - Journal of open source software

JF - Journal of open source software

IS - 97

M1 - 6338

ER -