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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.
git clone https://github.com/GrainLearning/grainLearning.git
cd grainLearning
poetry shell
poetry install
git clone https://github.com/GrainLearning/grainLearning.git
cd grainLearning
conda create --name grainlearning python=3.11 && conda activate grainlearning
pip install .
Developers please refer to README.dev.md.
To install GrainLearning including the RNN module capabilities check grainlearning/rnn/README.md.
Environments
and select grainlearning
from the
drop-down menuStable versions of GrainLearning can be installed via pip install grainlearning
However, you still need to clone the GrainLearning repository to run the tutorials.
Linear regression with
the run_sim
callback function of the BayesianCalibration
class,
in python_linear_regression_solve.py
Nonlinear, multivariate regression
Interact with the numerical model of your choice
via run_sim
,
in linear_regression_solve.py
Load existing DEM simulation data and run GrainLearning for one iteration, in oedo_load_and_resample.py
Example of GrainLearning integration into YADE
Data-driven module tutorials:
Please choose from the following:
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 by Hongyang Cheng and Stefan Luding with the help
of Luisa Orozco
and Retief Lubbe.
The GrainLearning project receives contributions from students and collaborators.
For assistance with the GrainLearning software, please create an issue on the GitHub Issues page.
This package was created with Cookiecutter and the NLeSC/python-template.