v2.0.4

What's Changed

• Integrate Geomechanics and dike examples into main by @chyalexcheng in #80
• add stopping criteria and error measures by @chyalexcheng in #82
• Update Linter.yml by @Retiefasaurus in #85

Full Changelog: v2.0.3...v2.0.4

GrainLearning v2.0.3

What's Changed

• Visuals by @chyalexcheng in #60
• Fix test errors while importing seaborn by @Retiefasuarus in #61
• Retiefasuarus/issue55 by @Retiefasuarus in #62
• Fix selftest errors by @chyalexcheng in #63
• 57 improving callback functions by @chyalexcheng in #66
• test workflow without python 3.8 by @chyalexcheng in #67
• Correct broken links to tutorials in rnn/README by @luisaforozco in #70
• Add note to windows installation via poetry by @luisaforozco in #74
• Update readthedocs script by @luisaforozco in #76
• Bump version and amend DOI by @luisaforozco in #77
• Fix release workflow by @luisaforozco in #78

Full Changelog: v2.0.2...v2.0.3

GrainLearning v2.0.2

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

Tutorials

• Linear regression with the run_sim callback function of the DynamicSystem class, in python_linear_regression_solve.py
• 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 with YADE in the directory /tutorials/physics_based/
• LSTM module tutorials:
  • Train your LSTM model
  • Predict using an LSTM model
  • Use an LSTM model in the calibration workflow

GL rewrite v1.0.1

Rewrite GrainLearing package

GrainLearning v0.2

GrainLearning version 0.2 contains the legacy code in Fortran and implementation in python.
The repository also contains data published in the paper, Cheng et al. (2019) An iterative Bayesian filtering framework for fast and automated calibration of DEM models. Comput. Methods Appl. Mech. Eng., 350 (2019), pp. 268-294