Skip to main content

A GPU accelerated Finite element analysis package in JAX.

Project description

Github Star Github Fork License

JAX-FEM

JAX-FEM is a differentiable finite element package based on JAX.

Documentation

For installation and user guide, please visit our documentation for details.

Key features

JAX-FEM is Automatic Differentiation (AD) + Finite Element Method (FEM), and we support the following features:

  • 2D quadrilateral/triangle elements
  • 3D hexahedron/tetrahedron elements
  • First and second order elements
  • Dirichlet/Neumann/Robin boundary conditions
  • Linear and nonlinear analysis including
    • Heat equation
    • Linear elasticity
    • Hyperelasticity
    • Plasticity (macro and crystal plasticity)
  • Multi-physics problems
  • Integration with PETSc for solver options
  • Differentiable programming for solving inverse/design problems without deriving sensitivities by hand, e.g.,
    • Topology optimization
    • Optimal thermal control

Examples

Thermal profile in direct energy deposition.

Linear static analysis of a bracket.

Crystal plasticity: grain structure (left) and stress-xx (right).

Stokes flow: velocity (left) and pressure(right).

Topology optimization with differentiable simulation.

License

This project is licensed under the GNU General Public License v3 - see the LICENSE for details. For commercial use, contact Tianju Xue.

Citations

If you found this library useful in academic or industry work, we appreciate your support if you consider 1) starring the project on Github, and 2) citing relevant papers:

@article{xue2023jax,
  title={JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science},
  author={Xue, Tianju and Liao, Shuheng and Gan, Zhengtao and Park, Chanwook and Xie, Xiaoyu and Liu, Wing Kam and Cao, Jian},
  journal={Computer Physics Communications},
  pages={108802},
  year={2023},
  publisher={Elsevier}
}

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

jax_fem-0.0.11.tar.gz (85.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

jax_fem-0.0.11-py3-none-any.whl (70.9 kB view details)

Uploaded Python 3

File details

Details for the file jax_fem-0.0.11.tar.gz.

File metadata

  • Download URL: jax_fem-0.0.11.tar.gz
  • Upload date:
  • Size: 85.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for jax_fem-0.0.11.tar.gz
Algorithm Hash digest
SHA256 3195f6405edfecd029f73f430102929625f96268595bab98d1b56efe8ce25dbe
MD5 e95b76cac23f3c04ddd4de3529d8dc6c
BLAKE2b-256 fa728f780d68aa90f617278f098797fdd70f478eb68a51f80e439f531a680296

See more details on using hashes here.

File details

Details for the file jax_fem-0.0.11-py3-none-any.whl.

File metadata

  • Download URL: jax_fem-0.0.11-py3-none-any.whl
  • Upload date:
  • Size: 70.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for jax_fem-0.0.11-py3-none-any.whl
Algorithm Hash digest
SHA256 58b921c5232626c8c2a4351cd88ae40cc5f6be8b4443c57eccaade10a4cca8ba
MD5 a84de2e8c7af02a0b4e789a52c8f7eb2
BLAKE2b-256 574d53f171e218e7d1ddfa29bfbb14d040e4fd358b586c460ce0ed991eae266a

See more details on using hashes here.

Supported by

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