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GrainLearning

GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for simulations of granular materials. It is primarily used to infer and quantify parameter uncertainties in computational models from observation data (i.e. inverse analyses or data assimilation).

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mentions
3
contributors

Cite this software

What GrainLearning can do for you

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, which is also known as inverse analyses or data assimilation. 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
Programming languages
  • Jupyter Notebook 84%
  • Python 15%
  • PureBasic 1%
License
  • GPL-2.0-only
</>Source code
Packages
pypi.org

Participating organisations

Netherlands eScience Center
University of Twente

Reference papers

Mentions

Contributors

HC
Hongyang Cheng
Principal investigator
University of Twente
Aron Jansen
Aron Jansen
eScience Research Engineer
Netherlands eScience Center

Related projects

GrainLearning

An artificial brain for interpreting and accelerating physics-based simulations of granular materials

Updated 5 months ago
Finished