# Simulation of Systems of interacting mean-field Particles with High Efficiency
<p align="center">
<img src="./doc/_static/ball.png" alt="logo" width="224"/>
</p>
Please visit the [website](https://sisyphe.readthedocs.io/en/latest/) for a full documentation.
------------------------------------------------------------------------------------------------
The SiSyPHE library builds on recent advances in hardware and software for the efficient simulation of **large scale interacting particle systems**, both on the **GPU** and on the CPU. The implementation is based on recent libraries originally developed for machine learning purposes to significantly accelerate tensor (array) computations, namely the [PyTorch](https://github.com/pytorch/pytorch) package and the [KeOps](https://www.kernel-operations.io/keops/index.html) library. The **versatile object-oriented Python interface** is well suited to the comparison of new and classical many-particle models, enabling ambitious numerical experiments and leading to novel conjectures. The SiSyPHE library speeds up both traditional Python and low-level implementations by **one to three orders of magnitude** for systems with up to **several millions** of particles.
<p align="center">
<img src="./doc/_static/mill.gif" alt="mill">
</p>
## Citation
If you use SiSyPHE in a research paper, please cite the [JOSS publication](https://joss.theoj.org/papers/10.21105/joss.03653#) :
```
@article{Diez2021,
doi = {10.21105/joss.03653},
url = {https://doi.org/10.21105/joss.03653},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3653},
author = {Antoine Diez},
title = {`SiSyPHE`: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency},
journal = {Journal of Open Source Software}
}
```
Diez, A., (2021). SiSyPHE: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency. Journal of Open Source Software, 6(65), 3653, https://doi.org/10.21105/joss.03653
[](https://doi.org/10.21105/joss.03653)
## Installation
### Requirements
- **Python 3** with packages **NumPy** and **SciPy**
- **PyTorch** : version>= 1.5
- **PyKeops** : version>= 1.5
### Using pip
In a terminal, type:
```
pip install sisyphe
```
### On Google Colab
The easiest way to get a working version of SiSyPHE is to use the free virtual machines provided by [Google Colab](https://colab.research.google.com).
1. On a new Colab notebook, navigate to Edit→Notebook Settings and select GPU from the Hardware Accelerator drop-down.
2. Install PyKeops with the Colab specifications **first** by typing
```
!pip install pykeops[colab]
```
3. Install SiSyPHE by typing
```
!pip install sisyphe
```
### Testing the installation
In a Python terminal, type
```python
import sisyphe
sisyphe.test_sisyphe()
```
<p align="center">
<img src="./doc/_static/band.gif" alt="band">
</p>
## Contributing
Contributions to make SiSyPHE grow are warmly welcome! Examples of possible (and ongoing) developments include the following.
* The implementation of new models.
* The implementation of more complex boundary conditions and of models on *non-flat* manifolds.
* An improved visualization method (currently only basic visualization functions relying on [Matplotlib](https://matplotlib.org/) are implemented).
Contributions can be made by opening an issue on the GitHub repository, via a pull request or by contacting directly the author.
## Author
- [Antoine Diez](https://antoinediez.gitlab.io), Imperial College London
### Acknowledgments
The development of this library would not have been possible without the help of [Jean Feydy](https://www.jeanfeydy.com/), his constant support and precious advice. This project was initiated by [Pierre Degond](https://sites.google.com/site/degond/) and has grown out of many discussions with him.
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