Published April 20, 2024
| Version v2.0.3
Software
Open
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
Creators
- 1. University of Twente
- 2. Netherlands eScience Center
- 3. University of Newcastle
Description
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.
Files
GrainLearning/grainLearning-v2.0.3.zip
Files
(10.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:491e97b49b2c73159ab9fd8c01e97456
|
10.1 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Software: https://github.com/GrainLearning/grainLearning/tree/v2.0.3 (URL)