dolfinx-external-operator 0.10.1

An implementation of ufl.ExternalOperator for DOLFINx

dolfinx-external-operator

dolfinx-external-operator is a implementation of the external operator concept in DOLFINx.

It allows for the expression of operators/functions in FEniCS that cannot be easily written in the Unified Form Language.

Potential application areas include complex constitutive models in solid and fluid mechanics, neural network constitutive models, multiscale modelling and inverse problems.

Implementations of external operators can be written in any library that supports the array interface protocol e.g. NumPy, JAX and Numba.

When using a library that supports program level automatic differentiation (AD), such as JAX, it is possible to automatically derive derivatives for use in local first and second-order solvers. Just-in-time compilation, batching and accelerators (GPUs, TPUs) are also supported.

Installation

dolfinx-external-operator is a pure Python package that depends on the DOLFINx Python interface and UFL. Version numbers match with compatible releases of DOLFINx.

The latest release version can be installed with:

pip install dolfinx-external-operator

The latest development version can be installed for development with:

git clone https://github.com/a-latyshev/dolfinx-external-operator.git
cd dolfinx-external-operator
pip install -e .

Documentation

The documentation contains various examples focusing on complex constitutive behaviour in solid mechanics, including:

• von Mises plasticity using Numba,
• Mohr-Coulomb plasticity using JAX.

Introduction to External Operators — FEniCS 2024 Conference:

<iframe width="700" height="400" src="https://www.youtube.com/embed/y8goeapqfsw?si=BQwDcdwqS3EF5rwW" frameborder="0" allowfullscreen></iframe>

Citations

If you use dolfinx-external-operator in your research we ask that you cite the following references:

@article{jtcam:14449,
  title      = {Expressing general constitutive models in FEniCSx using external operators and algorithmic automatic differentiation},
  author     = {Andrey Latyshev and Jérémy Bleyer and Corrado Maurini and Jack Hale},
  doi        = {10.46298/jtcam.14449},
  journal    = {Journal of Theoretical, Computational and Applied Mechanics},
  issn       = {2726-6141},
  year       = {2025},
  month      = {Sep},
  keywords   = {automated finite element solvers, Numba, JAX, FEniCSx, external operators, algorithmic automatic differentiation, constitutive models}
}

@inproceedings{latyshev_2024_external_paper,
  author = {Latyshev, Andrey and Bleyer, Jérémy and Hale, Jack and Maurini, Corrado},
  title = {A framework for expressing general constitutive models in FEniCSx},
  booktitle = {16ème Colloque National en Calcul de Structures},
  year = {2024},
  month = {May},
  publisher = {CNRS, CSMA, ENS Paris-Saclay, CentraleSupélec},
  address = {Giens, France},
  url = {https://hal.science/hal-04610881}
}

@software{latyshev_2024_external_code,
  title = {a-latyshev/dolfinx-external-operator},
  author = {Latyshev, Andrey and Hale, Jack},
  date = {2025},
  doi = {10.5281/zenodo.10907417},
  organization = {Zenodo}
}

Contributors

• Andrey Latyshev (University of Luxembourg, Sorbonne Université, [e-mail])
• Jérémy Bleyer (École des Ponts ParisTech, Université Gustave Eiffel, [e-mail])
• Jack S. Hale (University of Luxembourg, [e-mail])
• Corrado Maurini (Sorbonne Université, [e-mail])
• Jørgen S. Dokken (Simula Research Laboratory, [e-mail])

If you wish to be added as a contributor after an accepted PR please ask via email.

License

dolfinx-external-operator is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

dolfinx-external-operator is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with dolfinx-external-operator. If not, see https://www.gnu.org/licenses/.

Acknowledgments

This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference PRIDE/21/16747448/MATHCODA.

Developer notes

Docker container

docker run -ti -v $(pwd):/shared --entrypoint /bin/bash -w /shared docker.io/dolfinx/lab:nightly
pip install .

Building Documentation

pip install '.[doc]'
cd doc/
jupyter-book build .

and follow the instructions printed.

To continuously build and view the documentation in a web browser

pip install sphinx-autobuild
cd build/
jupyter-book config sphinx .
sphinx-autobuild . _build/html -b html

Linting

To lint and format

pip install '.[lint]'
ruff check .
ruff format .

Running tests

pip install '.[test]'
py.test -v test/

Releases

git pull
git checkout release
git merge --no-commit origin/main
git checkout --theirs . # files deleted on `main` must be manually git `rm`ed
vim pyproject.toml # Update version numbers
git diff origin/main # Check for mistakes
git tag v0.9.0 # for example
git push --tags origin

Then make a release using GitHub Releases.