https://github.com/chrisrackauckas/universal_differential_equations
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
https://github.com/chrisrackauckas/universal_differential_equations
neural-ode scientific-machine-learning sciml universal-differential-equations
Last synced: about 1 month ago
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Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
- Host: GitHub
- URL: https://github.com/chrisrackauckas/universal_differential_equations
- Owner: ChrisRackauckas
- License: mit
- Created: 2019-12-16T23:38:25.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-05T12:31:59.000Z (over 2 years ago)
- Last Synced: 2025-03-27T20:19:53.564Z (about 2 months ago)
- Topics: neural-ode, scientific-machine-learning, sciml, universal-differential-equations
- Language: Julia
- Homepage: https://arxiv.org/abs/2001.04385
- Size: 13.9 MB
- Stars: 222
- Watchers: 17
- Forks: 59
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Universal Differential Equations for Scientific Machine Learning (SciML)
Repository for the universal differential equations paper: [arXiv:2001.04385 [cs.LG]](https://arxiv.org/abs/2001.04385).
This repository is simply a demonstration. For more up-to-date versions of these codes, see the
[SciMLSensitivity.jl](https://docs.sciml.ai/SciMLSensitivity/stable/) and
[DiffEqFlux.jl](https://docs.sciml.ai/DiffEqFlux/stable/) documentations. Their tutorials incorporate all of the
ideas from this repo in well-maintained and tested software!
For more software, see the [SciML organization](https://sciml.ai/) and its [Github organization](https://github.com/SciML/)