Skip to content

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

License

Notifications You must be signed in to change notification settings

SciML/NeuralOperators.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

85 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

NeuralOperators.jl

Join the chat at https://julialang.zulipchat.com #sciml-bridged Global Docs

codecov Build Status Build status

ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

NeuralOperators.jl is a package written in Julia to provide the architectures for learning mapping between function spaces, and learning grid invariant solution of PDEs. Checkout the documentation for tutorials and API reference.

Installation

On Julia 1.10+, you can install NeuralOperators.jl by running

import Pkg
Pkg.add("NeuralOperators")

Citation

If you found this library to be useful in academic work, then please cite:

@software{pal2023lux,
  author    = {Pal, Avik},
  title     = {{Lux: Explicit Parameterization of Deep Neural Networks in Julia}},
  month     = apr,
  year      = 2023,
  note      = {If you use this software, please cite it as below.},
  publisher = {Zenodo},
  version   = {v0.5.0},
  doi       = {10.5281/zenodo.7808904},
  url       = {https://doi.org/10.5281/zenodo.7808904}
}

@thesis{pal2023efficient,
  title     = {{On Efficient Training \& Inference of Neural Differential Equations}},
  author    = {Pal, Avik},
  year      = {2023},
  school    = {Massachusetts Institute of Technology}
}

About

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Sponsor this project

 

Packages

No packages published