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nucml

End-to-end python-based supervised machine learning pipeline for ML-augmented nuclear data evaluation.

https://github.com/pedrojrv/nucml

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: sciencedirect.com
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

docker end-to-end-machine-learning nuclear supervised-learning
Last synced: 6 months ago · JSON representation

Repository

End-to-end python-based supervised machine learning pipeline for ML-augmented nuclear data evaluation.

Basic Info
Statistics
  • Stars: 21
  • Watchers: 3
  • Forks: 1
  • Open Issues: 1
  • Releases: 1
Topics
docker end-to-end-machine-learning nuclear supervised-learning
Created about 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Codeowners

README.md

NucML

<pedrojrv> Maintainability

NucML is 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. It offers capabilities that allows researchers to navigate through each step of the ML-based nuclear data cross section evaluation pipeline. Some of the supported activities include include dataset parsing and compilation of reaction data, exploratory data analysis, data manipulation and feature engineering, model training and evaluation, and validation via criticality benchmarks. Some of the inherit benefits of this approach are the reduced human-bias in the generation and solution and the fast iteration times. Resulting data from these models can aid the current NDE and help decisions in uncertain scenarios.

Installation and Setup

Please refer to the Installation guide in the official documentation here: https://pedrojrv.github.io/nucml/.

All educational and tutorial material can be found in the ML_Nuclear_Data repository here: https://github.com/pedrojrv/MLNuclearData

How to Cite

If you used NucML for your work, feel free to cite us:

@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}, }

Owner

  • Name: Pedro Vicente Valdez
  • Login: pedrojrv
  • Kind: user
  • Location: Berkeley, California
  • Company: Mythic

https://pedrojrv.github.io/

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 81
  • Total Committers: 3
  • Avg Commits per committer: 27.0
  • Development Distribution Score (DDS): 0.222
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
pedrojrv p****z@b****u 63
Pedro Vicente Valdez p****z@m****m 17
Pedro Vicente Valdez v****r@g****m 1
Committer Domains (Top 20 + Academic)
mythic-ai.com: 1 berkeley.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 1
  • Average time to close issues: 1 minute
  • Average time to close pull requests: 8 minutes
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • pedrojrv (2)
Pull Request Authors
  • pedrojrv (1)
Top Labels
Issue Labels
codeclimate (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 24 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: nucml

ML-oriented tools for navigating the nuclear data evaluation pipeline.

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 24 Last month
Rankings
Dependent packages count: 10.0%
Stargazers count: 14.5%
Average: 19.2%
Dependent repos count: 21.7%
Forks count: 22.6%
Downloads: 27.1%
Maintainers (1)
pvicentejr
Last synced: 6 months ago

Dependencies

.github/workflows/release-drafter.yml actions
  • release-drafter/release-drafter v5 composite
Dockerfile docker
setup.py pypi