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NeuralOperators.jl

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

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# NeuralOperators.jl

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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}
}
```

NeuralOperators.jl

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