Metadata-Version: 2.4
Name: uqinn
Version: 1.0.0
Summary: Library for augmenting NN with UQ
Home-page: https://github.com/XYZ
Author: Javier Murgoitio-Esandi, Oscar Diaz-Ibarra
Author-email: Khachik Sargsyan <ksargsy@sandia.gov>
License: BSD-3-Clause
Project-URL: Homepage, https://github.com/sandialabs/quinn
Project-URL: Repository, https://github.com/sandialabs/quinn
Keywords: uncertainty quantification,neural networks,UQ
Platform: BSD 3-clause
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Natural Language :: English
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: torch
Dynamic: home-page
Dynamic: license-file
Dynamic: platform

Quantification of Uncertainties in Neural Networks (QUiNN) is a python library centered around various probabilistic wrappers over PyTorch modules in order to provide uncertainty estimation in Neural Network (NN) predictions.

# Build the library
	./build.sh 
	or 
	./setup.py build; setup.py install

# Requirements
	numpy, scipy, matplotlib, pytorch

# Examples
	examples/ex_fit.py
	examples/ex_fit_2d.py
	examples/ex_ufit.py <method> # where method=mcmc, ens or vi.

# Authors
	Khachik Sargsyan
	Javier Murgoitio-Esandi
 	Oscar Diaz-Ibarra
  
# Acknowledgements
	This work is supported by 
	- U.S. Department of Energy, Office of Fusion Energy Sciences (OFES) under Field Work Proposal Number 20-023149.
	- Laboratory Directed Research and Development (LDRD) program of Sandia National Laboratories. 

