https://github.com/baggepinnen/diffeqsensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.1%) to scientific vocabulary
Repository
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.
Basic Info
- Host: GitHub
- Owner: baggepinnen
- License: other
- Default Branch: master
- Homepage: https://sensitivity.sciml.ai/dev
- Size: 10.9 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
DiffEqSensitivity.jl
DiffEqSensitivity.jl is a component package in the SciML Scientific Machine Learning ecosystem. It holds the sensitivity analysis utilities. Users interested in using this functionality should check out DifferentialEquations.jl.
Tutorials and Documentation
For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.
Owner
- Name: Fredrik Bagge Carlson
- Login: baggepinnen
- Kind: user
- Location: Lund, Sweden
- Website: baggepinnen.github.io
- Twitter: baggepinnen
- Repositories: 59
- Profile: https://github.com/baggepinnen
Control systems, system identification, signal processing and machine learning
Citation (CITATION.bib)
@article{rackauckas2020universal,
title={Universal differential equations for scientific machine learning},
author={Rackauckas, Christopher and Ma, Yingbo and Martensen, Julius and Warner, Collin and Zubov, Kirill and Supekar, Rohit and Skinner, Dominic and Ramadhan, Ali},
journal={arXiv preprint arXiv:2001.04385},
year={2020}
}
@article{DifferentialEquations.jl-2017,
author = {Rackauckas, Christopher and Nie, Qing},
doi = {10.5334/jors.151},
journal = {The Journal of Open Research Software},
keywords = {Applied Mathematics},
note = {Exported from https://app.dimensions.ai on 2019/05/05},
number = {1},
pages = {},
title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
volume = {5},
year = {2017}
}