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