https://github.com/axect/deeponet_template
https://github.com/axect/deeponet_template
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/axect/deeponet_template
- Owner: Axect
- License: mit
- Created: 2024-05-14T09:08:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-16T09:04:12.000Z (about 1 year ago)
- Last Synced: 2025-03-19T22:17:24.938Z (3 months ago)
- Language: Python
- Size: 14.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepONet Template Library
This is a template library for conducting research on DeepONet, a deep learning architecture for learning operators and solving partial differential equations. The library provides a modular and extensible framework for defining, training, and evaluating DeepONet models.
## Directory Structure
The template library has the following directory structure:
```
./
├── README.md
├── analyze.py
├── deeponet
│ ├── __init__.py
│ ├── data.py
│ ├── model.py
│ ├── train.py
│ └── utils.py
├── freeze.sh
├── kan.sh
├── requirements.txt
└── run.py
```
- `README.md`: This file, providing an overview and documentation of the library.
- `analyze.py`: A script for analyzing trained models and generating visualizations.
- `deeponet/`: The main package containing the core components of the library.
- `__init__.py`: Package initialization file.
- `data.py`: Module for loading and preprocessing data.
- `model.py`: Module defining the DeepONet model architectures.
- `train.py`: Module for training DeepONet models.
- `utils.py`: Module containing utility functions and classes.
- `freeze.sh`: A shell script for freezing the library's dependencies into `requirements.txt`.
- `kan.sh`: A shell script for cloning and setting up the Kolmogorov-Arnold Network (KAN) repository.
- `requirements.txt`: A file listing the library's dependencies.
- `run.py`: The main script for running experiments and training models.
## Prerequisites
- Python 3.7 or higher
- Git
- [uv](https://github.com/astral-sh/uv)
## Installation
To install the library and its dependencies, follow these steps:
1. Clone the repository:
```
git clone https://github.com/Axect/DeepONet_Template
```
2. Navigate to the library directory:
```
cd DeepONet_Template
```
3. Set up a virtual environment via uv:
```
uv venv
uv pip sync requirements.txt
source .venv/bin/activate
```
4. (Optional) Run the `kan.sh` script to download efficient-kan (Kolmogorov-Arnold Network):
```
sh kan.sh
```
## Usage
To use the DeepONet template library, follow these steps:
1. Prepare your data:
- Organize your data into the appropriate format required by the library.
- Modify the `data.py` module to load and preprocess your data.
2. Define your model:
- Choose the appropriate DeepONet model architecture from the `model.py` module or create a new one.
- Modify the model architecture and hyperparameters as needed.
3. Train your model:
- Use the `run.py` script to train your model.
- Adjust the training parameters and hyperparameters in the `run.py` script.
4. Analyze and visualize results:
- Use the `analyze.py` script to analyze the trained models and generate visualizations.
- Customize the analysis and visualization code in `analyze.py` based on your requirements.
5. (Optional) Freeze the dependencies:
- If you make changes to the library's dependencies, run the `freeze.sh` script to update the `requirements.txt` file.
## Contributing
Contributions to the DeepONet template library are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.
## License
This project is licensed under the [MIT License](LICENSE).
## Acknowledgments
This template library was inspired by various research papers and implementations of [DeepONet](https://github.com/lululxvi/deeponet).
We would like to acknowledge the contributors and researchers who have made significant contributions to the field of DeepONet and related areas.