NeuralOperator is a comprehensive PyTorch library for learning neural operators, containing the official implementation of Fourier Neural Operators and other neural operator architectures.

NeuralOperator is part of the PyTorch Ecosystem, check the PyTorch announcement!

Unlike regular neural networks, neural operators enable learning mapping between function spaces, and this library provides all of the tools to do so on your own data. Neural operators are resolution invariant, so your trained operator can be applied on data of any resolution.

Checkout the documentation and our practical guide for more information!

Just clone the repository and install locally (in editable mode so changes in the code are immediately reflected without having to reinstall):

git clone https://github.com/NeuralOperator/neuraloperator
cd neuraloperator
pip install -e .
pip install -r requirements.txt

You can also pip install the most recent stable release of the library on PyPI:

pip install neuraloperator

After you have installed the library, you can start training operators seamlessly:

from neuralop.models import FNO

operator = FNO(n_modes=(64, 64),
               hidden_channels=64,
               in_channels=2,
               out_channels=1)

Tensorization is also available: you can improve the previous models by simply using a Tucker Tensor FNO with fewer parameters:

from neuralop.models import TFNO

operator = TFNO(n_modes=(64, 64),
                hidden_channels=64,
                in_channels=2,
                out_channels=1,
                factorization='tucker',
                implementation='factorized',
                rank=0.1)

This will use a Tucker factorization of the weights. The forward pass will be efficient by contracting directly the inputs with the factors of the decomposition. The Fourier layers will have 10% of the parameters of an equivalent, dense Fourier Neural Operator!

To use W&B logging features, simply create a file in neuraloperator/config called wandb_api_key.txt and paste your W&B API key there.

NeuralOperator is 100% open-source, and we welcome contributions from the community!

Our mission for NeuralOperator is to provide access to well-documented, robust implementations of neural operator methods from foundations to the cutting edge, including new architectures, meta-algorithms, training methods and benchmark datasets. We are also interested in integrating interactive examples that showcase operator learning in action on small sample problems.

If your work provides one of the above, we would be thrilled to integrate it into the library. Otherwise, if your work simply relies on a version of the NeuralOperator codebase, we recommend publishing your code in a separate repository.

If you spot a bug or would like to see a new feature, please report it on our issue tracker or open a Pull Request.

For detailed development setup, testing, and contribution guidelines, please refer to our Contributing Guide.

All participants are expected to uphold the Code of Conduct to ensure a friendly and welcoming environment for everyone.

If you use NeuralOperator in an academic paper, please cite [1]:

@article{kossaifi2025librarylearningneuraloperators,
   author    = {Jean Kossaifi and
                  Nikola Kovachki and
                  Zongyi Li and
                  David Pitt and
                  Miguel Liu-Schiaffini and
                  Valentin Duruisseaux and
                  Robert Joseph George and
                  Boris Bonev and
                  Kamyar Azizzadenesheli and
                  Julius Berner and
                  Anima Anandkumar},
   title     = {A Library for Learning Neural Operators},
   journal   = {arXiv preprint arXiv:2412.10354},
   year      = {2025},
}

and consider citing [2], [3], [4]:

@article{duruisseaux2025guide,
   author    = {Valentin Duruisseaux and
                  Jean Kossaifi and
                  Anima Anandkumar},
   title     = {Fourier Neural Operators Explained: A Practical Perspective},
   journal   = {arXiv preprint arXiv:2512.01421},
   year      = {2025},
}

@article{kovachki2023neuraloperator,
   author    = {Nikola Kovachki and
                  Zongyi Li and
                  Burigede Liu and
                  Kamyar Azizzadenesheli and
                  Kaushik Bhattacharya and
                  Andrew Stuart and
                  Anima Anandkumar},
   title     = {Neural Operator: Learning Maps Between Function Spaces with Applications to PDEs},
   journal   = {JMLR},
   volume    = {24},
   number    = {1},
   articleno = {89},
   numpages  = {97},
   year      = {2023},
}

@article{berner2025principled,
   author    = {Julius Berner and
                  Miguel Liu-Schiaffini and
                  Jean Kossaifi and
                  Valentin Duruisseaux and
                  Boris Bonev and
                  Kamyar Azizzadenesheli and
                  Anima Anandkumar},
   title     = {Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning},
   journal   = {arXiv preprint arXiv:2506.10973},
   year      = {2025},
}