Metadata-Version: 2.4
Name: gfn-layer
Version: 0.1.0
Summary: Graph Feedforward Networks: an extension of linear layers to graphs
Author-email: "Oisín M. Morrison" <oisinmmm@gmail.com>
Maintainer-email: "Oisín M. Morrison" <oisinmmm@gmail.com>
License: Apache License Version 2.0
Project-URL: repository, https://github.com/Oisin-M/gfn
Project-URL: documentation, https://gfn-layer.readthedocs.io
Project-URL: issues, https://github.com/Oisin-M/gfn/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: scipy

# Graph Feedforward Network (GFN) - a novel neural network layer for resolution-invariant machine learning

<p align="center">
  <a href="https://doi.org/10.1016/j.cma.2024.117458">
    <img src="https://badgen.net/badge/10.1016/j.cma.2024.117458/red?icon=github/" alt="doi">
  </a>
  <!-- <a href="https://codecov.io/gh/Oisin-M/gfn">
    <img src="https://codecov.io/gh/Oisin-M/gfn/branch/develop/graph/badge.svg" alt="Code Coverage">
  </a> -->
  <a href="https://opensource.org/licenses/apache-2-0">
    <img src="https://img.shields.io/badge/Licence-Apache 2.0-blue.svg" alt="Licence">
  </a>
  <a href="https://github.com/Oisin-M/gfn/releases">
    <img src="https://img.shields.io/github/v/release/Oisin-M/gfn?color=purple&label=Release" alt="Latest Release">
  </a>
</p>

<p align="center">
  <a href="#why-gfns">Why GFNs?</a>
  •
  <a href="https://gfn-layer.readthedocs.io">Documentation</a>
  •
  <a href="#installation">Installation</a>
  •
  <a href="#quickstart">Quickstart</a>
  •
  <a href="#citing">Citing</a>
</p>

GFN is a generalisation of feedforward networks for graphical data.

> [!IMPORTANT]
> The code reproducing the results in the GFN-ROM paper has now been moved to [Oisin-M/GFN-ROM](https://github.com/Oisin-M/GFN-ROM).

## Why GFNs?
Many applications rely upon graphical data, which standard machine learning methods such as feedforward networks and convolutions cannot handle. GFNs present a novel approach of tackling this problem by extending existing machine learning approaches for use on graphical data. GFNs have very close links with neural operators and graph neural networks.

<p align="center">
    <img src="https://github.com/Oisin-M/gfn/raw/refs/heads/main/docs/images/gfn.png"/>
</p>

Key advantages of GFNs:
- Resolution invariance
- Equivalence to feedforward networks for single fidelity data (no deterioration in performance)
- Provable guarantees on performance for super- and sub-resolution
- Both fixed and adapative multifidelity training possible

## Installation

`gfn` is readily available on PyPI.
```
pip install gfn-layer
```
**Note:** the package name on PyPI is **gfn-layer**, gfn refers to a different package.

## Quickstart

See the [user guide](https://gfn-layer.readthedocs.io/en/latest/userguide.html) to get started!

## Citing
If this work is useful to you, please cite

[1] Morrison, O. M., Pichi, F. and Hesthaven, J. S. (2024) ‘GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications’. Available at: [arXiv](https://arxiv.org/abs/2406.03569) and [Computer Methods in Applied Mechanics and Engineering](https://doi.org/10.1016/j.cma.2024.117458)
```
@article{Morrison2024,
  title = {{GFN}: {A} graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications},
  author = {Morrison, Oisín M. and Pichi, Federico and Hesthaven, Jan S.},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  volume = {432},
  pages = {117458},
  year = {2024},
  doi = {10.1016/j.cma.2024.117458},
}
```
