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: 28 days 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 (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-05T12:31:59.000Z (about 2 years ago)
• Last Synced: 2024-10-17T07:21:06.220Z (3 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: 220
• Watchers: 18
• 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/)