/jax-fem

Differentiable Finite Element Method with JAX

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

A GPU-accelerated differentiable finite element analysis package based on JAX. Used to be part of the suite of open-source python packages for Additive Manufacturing (AM) research, JAX-AM.

Finite Element Method (FEM)

Github Star Github Fork License

FEM is a powerful tool, where 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)
  • Differentiable simulation for solving inverse/design problems without human deriving sensitivities, e.g.,
    • Topology optimization
    • Optimal thermal control
  • Integration with PETSc for solver choices

Updates (Dec 11, 2023):

  • We now support multi-physics problems in the sense that multiple variables can be solved monolithically. For example, consider running python -m applications.stokes.example
  • Weak form is now defined through volume integral and surface integral. We can now treat body force, "mass kernel" and "Laplace kernel" in a unified way through volume integral, and treat "Neumann B.C." and "Robin B.C." in a unified way through surface integral.

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.

Installation

Clone the repository:

git clone git@github.com:tianjuxue/jax-fem.git
cd jax-fem

Create a conda environment from the given environment.yml file:

conda env create -f environment.yml

Activate the environment and install the package:

conda activate jax-fem-env
pip install -e .

Dependencies

Install JAX

Quick start

Check demos/ for a variety of FEM cases. For example, run

python -m demos.hyperelasticity.example

for hyperelasticity.

Also,

python -m tests.benchmarks

will execute a set of test cases.

License

This project is licensed under the GNU General Public License v3 - see the LICENSE for details.

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}
}