| Name | jaxkan JSON |
| Version |
0.2.8
JSON |
| download |
| home_page | None |
| Summary | A JAX implementation of Kolmogorov-Arnold Networks |
| upload_time | 2025-04-09 10:55:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2024 Spyros Rigas, Michalis Papachristou
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
jax
nnx
kans
kolmogorov-arnold
pikan
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://doi.org/10.21105/joss.07830)
[](https://jaxkan.readthedocs.io/)
[](https://github.com/srigas/jaxKAN/blob/main/LICENSE)
[](https://github.com/srigas/jaxKAN/actions/workflows/test.yml)
[](https://pypi.org/project/jaxkan/)
# jaxKAN
jaxKAN is a Python package designed to enable the training of Kolmogorov-Arnold Networks (KANs) using the JAX framework. Built on Flax's NNX module, jaxKAN provides a collection of KAN layers that serve as foundational building blocks for various KAN architectures, such as the EfficientKAN and the ChebyKAN. While it includes standard features like initialization and forward pass methods, the KAN class in jaxKAN introduces an `extend_grids` method, which facilitates the extension of the grids for all layers in the network, irrespective of how those grids are defined. For instance, in the case of ChebyKAN, where a traditional grid concept doesn't exist, the method extends the order of the Chebyshev polynomials utilized in the model.
## Documentation
Extensive documentation on jaxKAN, including installation & contributing guidelines, API reference and tutorials, can be found [here](https://jaxkan.readthedocs.io/).
## Contributing
We warmly welcome community contributions to jaxKAN! For details on the types of contributions that will help jaxKAN evolve, as well as guidelines on how to contribute, visit [this](https://jaxkan.readthedocs.io/en/latest/contributing.html) page of our documentation.
## Citation
If you utilized `jaxKAN` for your own academic work, please use the following citation:
```
@article{Rigas2025,
author = {Rigas, Spyros and Papachristou, Michalis},
title = {jax{KAN}: A unified {JAX} framework for {K}olmogorov-{A}rnold Networks},
journal = {Journal of Open Source Software},
year = {2025},
volume = {10},
number = {108},
pages = {7830},
doi = {10.21105/joss.07830}
}
```
If you have used jaxKAN in your research for PIKAN-related applications or theoretical developments, please consider also citing the paper that originally introduced jaxKAN for these tasks:
```
@article{10763509,
author = {Rigas, Spyros and Papachristou, Michalis and Papadopoulos, Theofilos and Anagnostopoulos, Fotios and Alexandridis, Georgios},
title = {Adaptive Training of Grid-Dependent Physics-Informed {K}olmogorov-{A}rnold Networks},
journal = {IEEE Access},
year = {2024},
volume = {12},
pages = {176982-176998},
doi = {10.1109/ACCESS.2024.3504962}
}
```
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