# PhiFlow

**Repository Path**: simon2033/PhiFlow

## Basic Information

- **Project Name**: PhiFlow
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-25
- **Last Updated**: 2022-02-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ![PhiFlow](docs/figures/Logo1_layout.png)

![Build Status](https://github.com/tum-pbs/PhiFlow/actions/workflows/unit-tests.yml/badge.svg)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/phiflow.svg)](https://pypi.org/project/phiflow/)
[![PyPI license](https://img.shields.io/pypi/l/phiflow.svg)](https://pypi.org/project/phiflow/)
[![Code Coverage](https://codecov.io/gh/tum-pbs/PhiFlow/branch/develop/graph/badge.svg)](https://codecov.io/gh/tum-pbs/PhiFlow/branch/develop/)
[![Google Collab Book](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Fluids_Tutorial.ipynb)

Φ<sub>Flow</sub> is an open-source simulation toolkit built for optimization and machine learning applications.
It is written mostly in Python and can be used with
[NumPy](https://numpy.org/),
[PyTorch](https://pytorch.org/),
[Jax](https://github.com/google/jax)
or [TensorFlow](https://www.tensorflow.org/).
The close integration with these machine learning frameworks allows it to leverage their automatic differentiation functionality,
making it easy to build end-to-end differentiable functions involving both learning models and physics simulations.

This is major version 2 of Φ<sub>Flow</sub>.
Version 1 is available in the [branch `1.5`](https://github.com/tum-pbs/PhiFlow/tree/1.5) but will not receive new features anymore.
Older versions are available through the [release history](https://github.com/tum-pbs/PhiFlow/releases).

![Gui](https://tum-pbs.github.io/PhiFlow/figures/WebInterface.png)

## Features

* Variety of built-in PDE operations with focus on fluid phenomena, allowing for concise formulation of simulations.
* Tight integration with PyTorch, Jax and TensorFlow for straightforward neural network training with fully differentiable simulations that can [run on the GPU](https://tum-pbs.github.io/PhiFlow/GPU_Execution.html#enabling-gpu-execution).
* Flexible, easy-to-use [web interface](https://tum-pbs.github.io/PhiFlow/Web_Interface.html) featuring live visualizations and interactive controls that can affect simulations or network training on the fly.
* Object-oriented, vectorized design for expressive code, ease of use, flexibility and extensibility.
* Reusable simulation code, independent of backend and dimensionality, i.e. the exact same code can run a 2D fluid sim using NumPy and a 3D fluid sim on the GPU using TensorFlow or PyTorch.
* High-level linear equation solver with automated sparse matrix generation.

## Publications

* [Learning to Control PDEs with Differentiable Physics](https://ge.in.tum.de/publications/2020-iclr-holl/), *Philipp Holl, Vladlen Koltun, Nils Thuerey*, ICLR 2020.
* [Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers](https://arxiv.org/abs/2007.00016), *Kiwon Um, Raymond Fei, Philipp Holl, Robert Brand, Nils Thuerey*, NeurIPS 2020.
* [Φ<sub>Flow</sub>: A Differentiable PDE Solving Framework for Deep Learning via Physical Simulations](https://montrealrobotics.ca/diffcvgp/), *Nils Thuerey, Kiwon Um, Philipp Holl*, DiffCVGP workshop at NeurIPS 2020.

## Installation

Installation with pip on Python 3.6 and above:
``` bash
$ pip install phiflow dash plotly imageio
```
Install TensorFlow or PyTorch in addition to Φ<sub>Flow</sub> to enable machine learning capabilities and GPU execution.
See the [detailed installation instructions](https://tum-pbs.github.io/PhiFlow/Installation_Instructions.html) on how to compile the custom CUDA operators and verify your installation.

## Documentation and Guides
[**Documentation**](https://tum-pbs.github.io/PhiFlow/)
&nbsp;&nbsp;&nbsp; [**API**](https://tum-pbs.github.io/PhiFlow/phi/)
&nbsp;&nbsp;&nbsp; [**Demos**](https://github.com/tum-pbs/PhiFlow/tree/develop/demos)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [<img src="https://www.tensorflow.org/images/colab_logo_32px.png" height=16> **Playground**](https://colab.research.google.com/drive/1zBlQbmNguRt-Vt332YvdTqlV4DBcus2S#offline=true&sandboxMode=true)

An overview of all available documentation can be found [here](https://tum-pbs.github.io/PhiFlow/).

If you would like to get right into it and have a look at some code, check out the
[tutorial notebook on Google Colab](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/docs/Fluids_Tutorial.ipynb).
It lets you run fluid simulations with Φ<sub>Flow</sub> in the browser.

The following introductory demos are also helpful to get started with writing your own scripts using Φ<sub>Flow</sub>:

* [smoke_plume.py](demos/smoke_plume.py) runs a smoke simulation and displays it in the web interface.
* [optimize_pressure.py](demos/differentiate_pressure.py) uses TensorFlow to optimize a velocity field and displays it in the web interface.

## Version History

The [Version history](https://github.com/tum-pbs/PhiFlow/releases) lists all major changes since release.
The releases are also listed on [PyPI](https://pypi.org/project/phiflow/).

## Contributions

Contributions are welcome! Check out [this document](CONTRIBUTING.md) for guidelines.

## Acknowledgements

This work is supported by the ERC Starting Grant realFlow (StG-2015-637014) and the Intel Intelligent Systems Lab.