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ChrisRackauckas / universal_differential_equations

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Home Page:https://arxiv.org/abs/2001.04385

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universal-differential-equationsneural-odescimlscientific-machine-learning

Universal Differential Equations for Scientific Machine Learning (SciML)

Repository for the universal differential equations paper: arXiv:2001.04385 [cs.LG].

This repository is simply a demonstration. For more up-to-date versions of these codes, see the SciMLSensitivity.jl and DiffEqFlux.jl documentations. Their tutorials incorporate all of the ideas from this repo in well-maintained and tested software!

For more software, see the SciML organization and its Github organization

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Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

https://arxiv.org/abs/2001.04385

universal-differential-equationsneural-odescimlscientific-machine-learning

License:MIT License

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