GrainLearning 2.0.1

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

Welcome to GrainLearning!

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Documentation

Bayesian uncertainty quantification for discrete and continuum numerical models of granular materials, developed by various projects of the University of Twente (NL), the Netherlands eScience Center (NL), University of Newcastle (AU), and Hiroshima University (JP). Browse to the GrainLearning documentation to get started.

Features

• Infer and update model parameters using "time" series (sequence) data via Sequential Monte Carlo filtering
• Uniform, quasi-random sampling using low-discrepancy sequences
• Iterative sampling by training a nonparametric Gaussian mixture model
• Surrogate modeling capability for "time" series data

Installation

Install using poetry (recommended)

1. Install poetry following these instructions.
2. Clone the repository: git clone https://github.com/GrainLearning/grainLearning.git
3. Go to the source code directory: cd grainLearning
4. Activate the virtual environment: poetry shell
5. Install GrainLearning and its dependencies: poetry install
6. Run all self-tests of GrainLearning with pytest: poetry run pytest -v

For windows users, click here to check other installation options.

Tutorials

1. Linear regression with the run_sim callback function of the Model class
2. Nonlinear, multivariate regression
3. Interact with the numerical model of your choice
4. Load existing simulation data and run GrainLearning for one iteration

Citing GrainLearning

Please choose from the following:

• A DOI for citing the software
• 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

Software using GrainLearning

• YADE: http://yade-dem.org/
• MercuryDPM: https://www.mercurydpm.org/

Community

The original development of GrainLearning is done by Hongyang Cheng, in collaboration with Klaus Thoeni, Philipp Hartmann, and Takayuki Shuku. The software is currently maintained with the help of Luisa Orozco, Retief Lubbe, and Aron Jansen. The GrainLearning project receives contributions from students and collaborators. For an exhaustive list, see CONTRIBUTORS.md.

Help and Support

For assistance with the GrainLearning software, please raise an issue on the GitHub Issues page.

Credits

This package was created with Cookiecutter and the NLeSC/python-template.