Metadata-Version: 2.4
Name: neurop
Version: 0.0.5
Summary: Neural Operators!
Project-URL: Homepage, https://github.com/lonelyneutrin0/neurop
Author-email: Hrishikesh Belagali <belagal1@msu.edu>, Aditya Narayan <ma24btech11001@iith.ac.in>
License-Expression: MIT
License-File: LICENSE
Keywords: machine learning,neural operator
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.9
Requires-Dist: opt-einsum~=3.4.0
Requires-Dist: torch~=2.5.1
Provides-Extra: dev
Requires-Dist: hatchling~=1.27.0; extra == 'dev'
Requires-Dist: mypy~=1.13.0; extra == 'dev'
Requires-Dist: pdoc~=15.0.4; extra == 'dev'
Requires-Dist: pytest~=8.0.2; extra == 'dev'
Requires-Dist: ruff~=0.9.4; extra == 'dev'
Provides-Extra: examples
Requires-Dist: matplotlib~=3.10.3; extra == 'examples'
Description-Content-Type: text/markdown

# neurop 

[![Custom shields.io](https://img.shields.io/badge/docs-brightgreen?logo=github&logoColor=green&label=gh-pages)](https://lonelyneutrin0.github.io/neurop/)
[![PyPI version shields.io](https://img.shields.io/pypi/v/neurop.svg)](https://pypi.python.org/pypi/neurop/)
[![PyPI pyversions shields.io](https://img.shields.io/pypi/pyversions/neurop.svg)](https://pypi.python.org/pypi/neurop/)

## About neurop 
`neurop` is a Python package implementing [Neural Operators](https://en.wikipedia.org/wiki/Neural_operators#:~:text=Neural%20operators%20directly%20learn%20operators,be%20evaluated%20at%20any%20discretization.), which are a class of operating learning models. The package currently supports the following operator architectures -
- Fourier Neural Operator (FNO)
- Deep Operator Network (DeepONet)
- Complex Neural Operator (CoNO)

## Usage
`neurop` is published on [PyPi](https://pypi.python.org/pypi/neurop/) and can be installed using `pip` - 
```
pip install neurop
```

## License
`neurop` is available under the MIT License.

## Attribution
This project contains an independent implementation of the technique described in:

Karn Tiwari, N M Anoop Krishnan, Prathosh A P, "CoNO: Complex Neural Operator for Continuous Dynamical Systems," arXiv, 2023.  
Inspired by the code from [this repo](https://github.com/M3RG-IITD/Complex-Neural-Operator/tree/main), but not based on or derived from it.

