Skip to main content

Simple finite element assemblers with torch.

Project description

License: MIT Python Version

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torch-fem-0.0.1.tar.gz (210.1 kB view details)

Uploaded Source

Built Distribution

torch_fem-0.0.1-py3-none-any.whl (213.9 kB view details)

Uploaded Python 3

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

Hashes for torch-fem-0.0.1.tar.gz
Algorithm Hash digest
SHA256 e1417aa2606fee22aa9c9485c141b2580b67d07aff698e31842e88b2a5877ce5
MD5 65079bae3c4e4380bd444c6d9516dcb8
BLAKE2b-256 07f634831bca5d5f22f72bdf8859ff4347a0f52e71fe9400b82c1d2154f4ab30

See more details on using hashes here.

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

Hashes for torch_fem-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2f9898602369b40908fbc418f2d5467e2804ed30afc886a4dac37c7389d2f569
MD5 321086d3a9760281dcda892e8c35d19a
BLAKE2b-256 eac65c05fe628699520f20a53f8dc0b09f95668d6a886e9b06eb83d1d0125992

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page