Pedro Vicente-Valdez, PhD
Nuclear Engineering - UC Berkeley
[email protected]
Neutronics Lab - Massimiliano Fratoni, PhD
This repository contains tutorials and working code needed to tackle a complete end-to-end nuclear data evaluation. It is used by NucML, the first and only end-to-end python-based supervised machine learning pipeline for enhanced bias-free nuclear data generation and evaluation to support the advancement of next-generation nuclear systems.
The NucML documentation provides the best way to interact with the material in this repository, however, each directory contains a README.md file that explains the directories structure and content description for the contained files. Most jupyter notebooks are written in a way that explains the procedures and techniques being taken. Please visit the NucML documentation for instructions on installation and setup.
https://pedrojrv.github.io/nucml/index.html
If you use parts of these code, feel free to cite us using:
@article{VICENTEVALDEZ2021108596,
title = {Nuclear data evaluation augmented by machine learning},
journal = {Annals of Nuclear Energy},
volume = {163},
pages = {108596},
year = {2021},
issn = {0306-4549},
doi = {https://doi.org/10.1016/j.anucene.2021.108596},
url = {https://www.sciencedirect.com/science/article/pii/S0306454921004722},
author = {Pedro Vicente-Valdez and Lee Bernstein and Massimiliano Fratoni},
keywords = {Machine learning, EXFOR, Uranium benchmark, Cross section evaluation},
}