PhiFlow

by tum-pbs

tum-pbs / PhiFlow

Research-oriented differentiable fluid simulation framework

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ΦFlow

Build Status PyPI pyversions PyPI license Google Collab Book

Gui

ΦFlow is a research-oriented, open-source PDE solving toolkit that is fully differentiable. It is written mostly in Python and can use both NumPy, TensorFlow and PyTorch for execution.

Having all functionality of a fluid simulation running in a deep learning framework opens up the possibility of back-propagating gradients through the simulation as well as running the simulation on GPUs.

Features

  • Variety of built-in fully-differentiable simulations, ranging from Burgers and Navier-Stokes to the Schrödinger equation.
  • Tight integration with TensorFlow and PyTorch (experimental) allowing for straightforward neural network training with fully differentiable simulations that run on the GPU.
  • Object-oriented architecture enabling concise and expressive code, designed for ease of use 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.
  • Flexible, easy-to-use web interface featuring live visualizations and interactive controls that can affect simulations or network training on the fly.

Publications

Installation

To install ΦFlow with its web interface, run:

$ pip install phiflow[gui]

Install TensorFlow or PyTorch in addition to ΦFlow to enable machine learning capabilities and GPU execution.

See the detailed installation instructions on how to compile the custom CUDA operators and verify your installation.

Documentation and Guides

| Index | Demos / Tests | Source | Fluids Tutorial / Playground | |------------------------|---------------------------------|---------------| -----------------------------|

If you would like to get right into it and have a look at some code, check out the tutorial notebook on Google Colab. It lets you run fluid simulations with ΦFlow in the browser.

The following introductory demos are also helpful to get started with writing your own app using ΦFlow:

  • simpleplume.py: Runs a fluid simulation and displays it in the browser
  • optimize_pressure.py: Uses TensorFlow to optimize a velocity channel. TensorBoard can be started from the GUI and displays the loss.

Running simulations

The simulation overview explains how to run predefined simulations using either the NumPy or TensorFlow backend. It also introduces the GUI.

To learn how specific simulations are implemented, check out the documentation for Fluids or read about staggered grids or pressure solvers.

The ΦFlow Web Interface guide introduces the high-level classes and explains how to launch and configure the built-in web interface for displaying simulations and interactive network training.

For I/O and data management, see the data documentation.

Optimization and Learning

For training machine learning models, this document gives an introduction into writing a GUI-enabled application.

Architecture

The simulation code design documentation provides a deeper look into the object-oriented code design of simulations.

All simulations of continuous systems are based on the Field API and underlying all states is the struct API.

The software architecture documentation shows the building blocks of ΦFlow and the module dependencies.

Version History

The Version history lists all major changes since release.

Known Issues

TensorBoard: Live supervision does not work when running a local app that writes to a remote directory.

Resampling / Advection: NumPy interpolation handles the boundaries slightly differently than TensorFlow.

Contributions

Contributions are welcome! Check out this document for guidelines.

Acknowledgements

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

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