ML-oriented tools for navingating the nuclear data evaluation pipeline.
Project description
NucML
Pedro Vicente-Valdez
Nuclear Engineering PhD Candidate - UC Berkeley
pedro.vicentevz@berkeley.edu
Neutronics Group
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/getting-started.html.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for nucml-1.0.0.dev1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c5685084d787f4e25779e638fdcf4b2c14c56e376b92f3a824fc4993587dc9b5 |
|
MD5 | 4173483774def67b08063acaae073758 |
|
BLAKE2b-256 | f4b038e5d6a1f71196b0bef3d06519667036c71375a146555d13ee14d52d3db9 |