Differentiable PDE solving framework for machine learning
Project description
PhiFlow
Homepage Documentation API Demos Fluids Tutorial Playground
PhiFlow 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, TensorFlow, Jax or PyTorch. The close integration with 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.
See the installation Instructions on how to compile the optional custom CUDA operations.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file phiflow-3.1.0.tar.gz
.
File metadata
- Download URL: phiflow-3.1.0.tar.gz
- Upload date:
- Size: 182.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6f5f036b56d40f75dcb14f5df0a72864e208fd6d30c1e5431064308301ddde63 |
|
MD5 | 02f601d9161dd80bffb5ce50fb85e36b |
|
BLAKE2b-256 | 2d003ac8b73847573d75671e2960514fb9ae6821a16e63fe4dd4f69d87319d71 |