https://github.com/SciML/NeuralOperators.jl

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
https://github.com/SciML/NeuralOperators.jl

automatic-differentiation deep-learning deeponet differential-equations fourier-neural-operator fourier-transform gnn julia operator partial-differential-equations pde scientific-machine-learning

Last synced: 2 months ago
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DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

• Host: GitHub
• URL: https://github.com/SciML/NeuralOperators.jl
• Owner: SciML
• License: mit
• Created: 2023-12-10T05:52:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-08T17:22:31.000Z (8 months ago)
• Last Synced: 2024-11-08T18:26:48.516Z (8 months ago)
• Language: Julia
• Homepage: https://docs.sciml.ai/NeuralOperators/stable/
• Size: 4.69 MB
• Stars: 12
• Watchers: 5
• Forks: 4
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-sciml - foldfelis/NeuralOperators.jl: learning the solution operator for partial differential equations in pure Julia.

README

        
# NeuralOperators.jl

[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)

[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/NeuralOperators/stable/)

[![codecov](https://codecov.io/gh/SciML/NeuralOperators.jl/branch/main/graph/badge.svg?token=wTS4cxrvB1)](https://codecov.io/gh/SciML/NeuralOperators.jl)

[![Build Status](https://github.com/SciML/NeuralOperators.jl/workflows/CI/badge.svg)](https://github.com/SciML/NeuralOperators.jl/actions?query=workflow%3ACI)

[![Build status](https://badge.buildkite.com/dd09599b08f61de1b5c7960aacd5390554c53e3b54f1407ba1.svg?branch=main)](https://buildkite.com/julialang/neuraloperators-dot-jl)

[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)

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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](https://docs.sciml.ai/NeuralOperators/stable/) for tutorials and API

reference.

## Installation

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

```julia

import Pkg

Pkg.add("NeuralOperators")

```

## Citation

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

```bibtex

@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}

}

```