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https://github.com/sandialabs/pancax

A set of tools for developing new methods and techniques in physics informed neural networks written in jax.
https://github.com/sandialabs/pancax

equinox jax physics physics-informed-neural-networks physics-simulation

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A set of tools for developing new methods and techniques in physics informed neural networks written in jax.

Host: GitHub
URL: https://github.com/sandialabs/pancax
Owner: sandialabs
License: mit
Created: 2024-11-06T14:17:33.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-11-29T16:52:21.000Z (2 months ago)
Last Synced: 2024-11-29T17:33:34.866Z (2 months ago)
Topics: equinox, jax, physics, physics-informed-neural-networks, physics-simulation
Language: Python
Homepage:
Size: 5.75 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

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# Pancax ("PANCAKES")

Physics augmented neural computations in jax

## Table of Contents

1. [Installation](#installation)

2. [Usage](#usage)

3. [Citation](#citation)

## Installation

### CPU installation instructions

To install pancax using pip (recommended) for CPU usage you can type the following command

``pip install pancax[cpu]``

### GPU installation instructions

Currently only CUDA has been tested, so only a CUDA option is supplied.

#### CUDA installation instructions

To install pancax using pip (recommended) for CPU usage you can type the following command

``pip install pancax[cuda]``

### Developer installation instructions

If you would like to do development in pancax, please first clone the repo and in the pancax 

folder, run the following command

``pip install -e .[cuda,docs,test]``

## Usage

Currently the main entry point to pancax is through a python script (although a yaml input file is also in the works).

To run a script you can run the following command

``python -m pancax -i my_script.py``

where ``my_script.py`` is the name of the scipt you've written. This will run the python script while also 

respecting several environment variables which can be supplied after the ``pancax`` keyword above. A list of

these can be displayed with the help message

``python -m pancax -h``

![Pancax](https://github.com/sandialabs/pancax/blob/main/assets/pancax.png?raw=true)

If you leverage these tools for your own research, please cite the following article

## Citation

```bibtex

@article{hamel2023calibrating,

  title={Calibrating constitutive models with full-field data via physics informed neural networks},

  author={Hamel, Craig M and Long, Kevin N and Kramer, Sharlotte LB},

  journal={Strain},

  volume={59},

  number={2},

  pages={e12431},

  year={2023},

  publisher={Wiley Online Library}

}

```

SCR #3050.0