Simple finite element assemblers with torch.
Project description
torch-fem: Differentiable linear elastic finite elements
Simple finite element assemblers for linear elasticity with PyTorch. The advantage of using PyTorch is the ability to efficiently compute sensitivities and use them in structural optimization.
Installation
Your may install torch-fem via pip by running
pip install .
in the torch-fem directory.
Examples
The subdirectory examples->basic
contains a couple of Jupyter Notebooks demonstrating the use of torch-fem for trusses, planar problems and solid problems. The subdirectory examples->optimization
demonstrates the use of torch-fem for optimization of structures (e.g. topology optimization, composite orientation optimization).
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
Built Distribution
File details
Details for the file torch-fem-0.0.1.tar.gz
.
File metadata
- Download URL: torch-fem-0.0.1.tar.gz
- Upload date:
- Size: 210.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e1417aa2606fee22aa9c9485c141b2580b67d07aff698e31842e88b2a5877ce5 |
|
MD5 | 65079bae3c4e4380bd444c6d9516dcb8 |
|
BLAKE2b-256 | 07f634831bca5d5f22f72bdf8859ff4347a0f52e71fe9400b82c1d2154f4ab30 |
File details
Details for the file torch_fem-0.0.1-py3-none-any.whl
.
File metadata
- Download URL: torch_fem-0.0.1-py3-none-any.whl
- Upload date:
- Size: 213.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2f9898602369b40908fbc418f2d5467e2804ed30afc886a4dac37c7389d2f569 |
|
MD5 | 321086d3a9760281dcda892e8c35d19a |
|
BLAKE2b-256 | eac65c05fe628699520f20a53f8dc0b09f95668d6a886e9b06eb83d1d0125992 |