EFBMC - Elastic Functional Bayesian Model Calibration

For Python, R, and MATLAB. There is two example notebooks. The one with _toy in the filename is a toy dataset. The other is an example calibration Equation of State model from the Z-machine.

Getting started with Python

For the python code you need to have python 3.9 or later installed. We recommend anaconda and you can create an conda environment with the needed requirements using the following command

conda create -n EFBMC -c conda-forge fdasrsf seaborn scikit-learn pytest pandas pybind11 jupyterlab
conda activate EFBMC
pip install git+https://github.com/lanl/pyBASS.git
pip install git+https://github.com/gqcollins/pyBayesPPR.git
pip install "git+https://github.com/sandialabs/mvBayesPy"
pip install git+https://github.com/lanl/impala/impala.git

to install other surrogate models (e.g., BART (stochtree))

pip install git+https://github.com/StochasticTree/stochtree.git

You then can run the example notebook above

Getting started with R

For the R code you will need to the following R packages installed

install.packages(c("fdasrvf","BASS","stochtree","bayesplot","readr","interp","hexbin","remotes"))
library(remotes)
install_git("https://github.com/sandialabs/rImpala")
install_git("https://github.com/sandialabs/mvBayesR")

Getting started with MATLAB

For the MATLAB code you will need to the following MATLAB packages in your path

1. impala
2. fdasrvf
3. mvBayes
4. BASS
5. BayesPPR

References

D. Francom, J. D. Tucker, J. G. Huerta, K. Shuler, and D. Ries, "Elastic Bayesian Model Calibration", SIAM/ASA Journal on Uncertainty Quantification, vol. 13, no. 1, 2025. PDF

J. Brown, J. P. Davis, J. D. Tucker, J. G. Huerta, and K. Shuler, "Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum, Part 2: Bayesian model calibration", Journal of Applied Physics, vol. 134, no. 23, 2023. PDF

Contact

For questions please contact J. Derek Tucker (5573) or Gavin Collins (5573)
jdtuck@sandia.gov
gqcolli@sandia.gov