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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: GrainLearning
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Hongyang
family-names: Cheng
email: [email protected]
affiliation: University of Twente
orcid: 'https://orcid.org/0000-0001-7652-8600'
- given-names: Luisa
family-names: ' Orozco'
email: [email protected]
orcid: 'https://orcid.org/0000-0002-9153-650X'
affiliation: Netherlands eScience Center
- given-names: Retief
family-names: Lubbe
email: [email protected]
affiliation: University of Twente
- given-names: Aron
family-names: Jansen
orcid: 'https://orcid.org/0000-0002-4764-9347'
affiliation: Netherlands eScience Center
email: [email protected]
- given-names: Philipp
family-names: Hartmann
email: [email protected]
orcid: 'https://orcid.org/0000-0002-2524-8024'
affiliation: University of Newcastle
- given-names: Klaus
family-names: Thoeni
orcid: 'https://orcid.org/0000-0001-7351-7447'
affiliation: University of Newcastle
email: [email protected]
identifiers:
- type: doi
value: 10.5281/zenodo.7123965
repository-code: 'https://github.com/GrainLearning/grainLearning/'
url: 'https://grainlearning.readthedocs.io/'
abstract: >-
GrainLearning is a Bayesian uncertainty quantification and
propagation toolbox for computer simulations of granular
materials. The software is primarily used to infer and
quantify parameter uncertainties in computational models
of granular materials from observation data, also known as
inverse analyses or data assimilation. Implemented in
Python, GrainLearning can be loaded into a Python
environment to process the simulation and observation
data, or alternatively, as an independent tool where
simulation runs are done separately, e.g., via a shell
script.
keywords:
- Uncertainty quantification
- Granular materials
- Machine learning
- Calibration
- Bayesian inference
license: GPL-2.0