Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.7%) to scientific vocabulary
Keywords
Repository
PyQBMMlib is a Python extension of QBMMlib
Basic Info
Statistics
- Stars: 9
- Watchers: 3
- Forks: 8
- Open Issues: 2
- Releases: 1
Topics
Metadata Files
README.md
PyQBMMlib
PyQBMMlib is a Python fork of QBMMlib, which was developed by Prof. Spencer Bryngelson, Prof. Rodney Fox, and Prof. Tim Colonius.
It can be cited as
@article{bryngelson_2020,
Author = {Spencer H. Bryngelson and Tim Colonius and Rodney O. Fox},
Title = {{QBMMlib: A} library of quadrature-based moment methods},
Journal = {SoftwareX},
Volume = {12},
Pages = {100615},
Year = {2020},
}
When compared to QBMMlib, PyQBMMlib offers significantly decreased time to solution (when using Numba).
PyQBMMlib is used to train new QBMMs based on neural networks in this paper.
Authors:
- Spencer H. Bryngelson (Georgia Tech)
- Esteban Cisneros (Princeton)
Required Python modules
- Python >= 3.0
- Numpy
- Scipy
- Sympy
- Optional: Numba (significant speedup via JIT compiling)
Current capabilities
PyQBMMlib is under active development. However, it still has all the capabilities of QBMMlib except for traditional CQMOM, which was elided in lieu of its contemporary, CHyQMOM. This includes: - Automatic formulation of moment transport equations - 1-3D moment inversion - QMOM (Wheeler), HyQMOM (2 and 3 node), CHyQMOM (4, 9, and 27 node) - SSP RK2-3
Features under development
Several more features will be added to PyQBMMlib. A partial list is included here. - 2D + static 1D moment inversion - Spatial dependencies and fluxes (3D flows)
Acknowledgements
Great thanks is owed to Professor Alberto Passalacqua (Iowa State University) for his part in developing these algorithms and teaching me the same. We acknowledge funding from the U.S. Office of Naval Research under grant numbers N0014-17-1-2676 and N0014-18-1-2625 (SHB) and the U.S. Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002374 (ECG).
License
PyQBMMlib is under the MIT license.
Owner
- Name: Computational Physics @ GT CSE
- Login: comp-physics
- Kind: organization
- Email: shb@gatech.edu
- Website: https://comp-physics.group
- Repositories: 8
- Profile: https://github.com/comp-physics
A computational physics research group with PI Spencer Bryngelson
Citation (CITATION.cff)
cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Bryngelson
given-names: Spencer
title: comp-physics/PyQBMMlib
version: v0.1
date-released: 2022-10-31
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Dependencies
- PyYAML ==5.4.1
- numba ==0.54.0
- numpy ==1.19.2
- pytest ==6.2.5
- scipy ==1.5.4
- sympy ==1.7.1
- actions/checkout v2 composite
- actions/setup-python v2 composite