https://github.com/aai-institute/continuiti

Learning function operators with neural networks.
https://github.com/aai-institute/continuiti

neural-operators physics-informed-ml transferlab

Last synced: 5 months ago
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Learning function operators with neural networks.

• Host: GitHub
• URL: https://github.com/aai-institute/continuiti
• Owner: aai-institute
• License: lgpl-3.0
• Created: 2023-10-26T08:46:20.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-22T09:11:39.000Z (almost 2 years ago)
• Last Synced: 2026-01-06T16:37:22.545Z (5 months ago)
• Topics: neural-operators, physics-informed-ml, transferlab
• Language: Python
• Homepage:
• Size: 22.4 MB
• Stars: 35
• Watchers: 2
• Forks: 2
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt

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README

          




continuiti


Learning function operators with neural networks.

[![PyTorch](https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)

[![Documentation](https://img.shields.io/badge/Documentation-blue)](https://aai-institute.github.io/continuiti/)

[![Test](https://github.com/aai-institute/continuiti/actions/workflows/test.yml/badge.svg)](https://github.com/aai-institute/continuiti/actions/workflows/test.yml)



**continuiti** is a Python package for deep learning on function operators with

a focus on elegance and generality. It provides a _unified interface_ for neural

operators (such as DeepONet or FNO) to be used in a plug and play fashion. As

operator learning is particularly useful in scientific machine learning,

**continuiti** also includes physics-informed loss functions and a collection of

relevant benchmarks.

## Installation

Install the package using pip:

```shell

pip install continuiti

```

Or install the latest development version from the repository:

```

git clone https://github.com/aai-institute/continuiti.git

cd continuiti

pip install -e ".[dev]"

```

## Usage

Our [Documentation](https://aai-institute.github.io/continuiti/) contains a

collection of tutorials on how to learn operators using continuiti,

a collection of how-to guides to solve specific problems, more background,

and a class documentation.

In general, the operator syntax in **continuiti** is

```python

v = operator(x, u(x), y)

```

mapping a function `u` (evaluated at `x`) to function `v` (evaluated in `y`).

## Examples





Fourier Neural Operator (FNO) for Navier-Stokes flow












Physics-informed DeepONet








## Contributing

Contributions are welcome from anyone in the form of pull requests, bug reports

and feature requests. If you find a bug or have a feature request, please open

an issue on GitHub. If you want to contribute code, please fork the repository

and submit a pull request. See [CONTRIBUTING.md](CONTRIBUTING.md) for details on

local development.

## License

This project is licensed under the GNU LGPLv3 License - see the

[LICENSE](LICENSE) file for details.