neuraloperator is a comprehensive library for
learning neural operators in PyTorch.
It is the official implementation for Fourier Neural Operators
and Tensorized Neural Operators.
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.
NeuralOperators are also resolution invariant, so your trained operator can be applied on data of any resolution.
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 just pip install the library:
pip install neuraloperator
After you've installed the library, you can start training operators seemlessly:
from neuralop.models import FNO
operator = FNO(n_modes=(16, 16), hidden_channels=64,
in_channels=3, out_channels=1)Tensorization is also provided out of the box: you can improve the previous models by simply using a Tucker Tensorized FNO with just a few parameters:
from neuralop.models import TFNO
operator = TFNO(n_modes=(16, 16), hidden_channels=64,
in_channels=3,
out_channels=1,
factorization='tucker',
implementation='factorized',
rank=0.05)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 5% of the parameters of an equivalent, dense Fourier Neural Operator!
Checkout the documentation for more!
Create a file in neuraloperator/config called wandb_api_key.txt and paste your Weights and Biases API key there.
You can configure the project you want to use and your username in the main yaml configuration files.
All contributions are welcome! So if you spot a bug or even a typo or mistake in
the documentation, please report it, and even better, open a Pull-Request on
GitHub. Before you submit
your changes, you should make sure your code adheres to our style-guide. The
easiest way to do this is with black:
pip install black black .
Testing and documentation are an essential part of this package and all
functions come with uni-tests and documentation. The tests are ran using the
pytest package. First install pytest:
pip install pytest
Then to run the test, simply run, in the terminal:
pytest -v neuralop
If you use NeuralOperator in an academic paper, please cite [1], [2]:
@misc{li2020fourier,
title={Fourier Neural Operator for Parametric Partial Differential Equations},
author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
year={2020},
eprint={2010.08895},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@article{kovachki2021neural,
author = {Nikola B. Kovachki and
Zongyi Li and
Burigede Liu and
Kamyar Azizzadenesheli and
Kaushik Bhattacharya and
Andrew M. Stuart and
Anima Anandkumar},
title = {Neural Operator: Learning Maps Between Function Spaces},
journal = {CoRR},
volume = {abs/2108.08481},
year = {2021},
}
| [1] | Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar A., “Fourier Neural Operator for Parametric Partial Differential Equations”, ICLR, 2021. doi:10.48550/arXiv.2010.08895. |
| [2] | Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar A., “Neural Operator: Learning Maps Between Function Spaces”, JMLR, 2021. doi:10.48550/arXiv.2108.08481. |
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent