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jaxKAN

(★ 112)

Kolmogorov–Arnold Networks built on JAX

파일 탐색기

최종 버전 다운로드 (.zip)

- - publish-to-pypi.yml
  - test.yml
- - index.rst
  - Tutorial 1 - Getting Started.ipynb
  - Tutorial 10 - Advanced Models for PDEs.ipynb
  - Tutorial 11 - Optimizer Utilities.ipynb
  - Tutorial 2 - Layer Types.ipynb
  - Tutorial 3 - Initialization Types.ipynb
  - Tutorial 4 - Classification Example.ipynb
  - Tutorial 5 - DIY KANs.ipynb
  - Tutorial 6 - PIKANs.ipynb
  - Tutorial 7 - Adaptive State Transition.ipynb
  - Tutorial 8 - Adaptive PIKANs.ipynb
  - Tutorial 9 - Inverse PDE Problem.ipynb
- about.rst
- api-reference.rst
- citing.rst
- conf.py
- contributing.rst
- index.rst
- installation.rst
- - __init__.py
  - BaseGrid.py
  - RBFGrid.py
  - SplineGrid.py
- - __init__.py
  - Chebyshev.py
  - Dense.py
  - Fourier.py
  - Legendre.py
  - RBF.py
  - Sine.py
  - Spline.py
  - utils.py
- - __init__.py
  - ActNet.py
  - KAN.py
  - KKAN.py
  - RGAKAN.py
  - utils.py
- - __init__.py
  - adaptive.py
  - pde.py
  - sampling.py
  - utils.py
- __init__.py
- - README.md
- - paper.bib
  - paper.md
- - test_chebyshev_layer.py
  - test_dense_layer.py
  - test_fourier_layer.py
  - test_layer_utils.py
  - test_legendre_layer.py
  - test_rbf_layer.py
  - test_sine_layer.py
  - test_spline_layer.py
- - test_Act.py
  - test_KAN.py
  - test_KKAN.py
  - test_model_utils.py
  - test_RGAKAN.py
- - test_adaptive.py
  - test_pde.py
  - test_pikan_utils.py
  - test_sampling.py
- conftest.py
.gitattributes
.gitignore
.readthedocs.yaml
LICENSE
pyproject.toml
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/srigas/jaxKAN

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd jaxKAN

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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[![DOI](https://joss.theoj.org/papers/10.21105/joss.07830/status.svg)](https://doi.org/10.21105/joss.07830)
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# 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}
}
```