References
==========

.. _references:

This section lists all the references used in the `fair-sciml` project.

- Alnæs, M. S., Blechta, J., Hake, J., Johansen, H., & Størseth, M. (2015). *DOLFINx: A Python-based library for solving partial differential equations* [Software Documentation]. FEniCS Project. Retrieved from `https://docs.fenicsproject.org/dolfinx/main/python/index.html <https://docs.fenicsproject.org/dolfinx/main/python/index.html>`_.

- Alvarez, E., Escapil, P., & Parra, A. (2024). *FAIR Scientific Machine Learning* [Documentation]. ReadTheDocs. Retrieved from `https://fair-sciml.readthedocs.io/en/latest/ <https://fair-sciml.readthedocs.io/en/latest/>`_.

- Data Observatory. (n.d.). *Data Observatory*. Retrieved from `https://www.dataobservatory.net/ <https://www.dataobservatory.net/>`_.

- Docker, Inc. (n.d.). *Docker: Enterprise container platform*. Retrieved from `https://www.docker.com/ <https://www.docker.com/>`_.

- Escapil, P., Alvarez, E., & Parra, A. (2024). *FAIR Scientific Machine Learning* [Code Repository]. GitHub. Retrieved from `https://github.com/pescap/fair-sciml <https://github.com/pescap/fair-sciml>`_.

- European Synchrotron Radiation Facility (ESRF). (2024). *myhdf5: Online HDF5 file viewing service*. Web Application. Retrieved from `https://myhdf5.hdfgroup.org/ <https://myhdf5.hdfgroup.org/>`_.

- Evans, L. C. (1998). *Partial differential equations (Vol. 19)*. Providence, RI: American Mathematical Society.

- FEniCS Project. (n.d.a). *FEniCS*. Retrieved from `https://fenicsproject.org/ <https://fenicsproject.org/>`_.

- FEniCS Project. (n.d.b). *Poisson equation demo* [Code Example]. Retrieved from `https://olddocs.fenicsproject.org/dolfin/latest/python/demos/poisson/demo_poisson.py.html <https://olddocs.fenicsproject.org/dolfin/latest/python/demos/poisson/demo_poisson.py.html>`_.

- Hugging Face. (n.d.). *Hugging Face*. Retrieved from `https://huggingface.co/ <https://huggingface.co/>`_.

- Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2021). *Fourier neural operator for parametric partial differential equations*. In International conference on learning representations (ICLR). Retrieved from `https://arxiv.org/abs/2010.08895 <https://arxiv.org/abs/2010.08895>`_.

- Lu, L., Jin, P., & Karniadakis, G. E. (2019). *DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators*. arXiv preprint. doi: `10.48550/arXiv.1910.03193 <https://doi.org/10.48550/arXiv.1910.03193>`_.

- Lu, L., Meng, X., Cai, S., Mao, Z., Goswami, S., Zhang, Z., & Karniadakis, G. E. (2022a). *A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data*. Computer Methods in Applied Mechanics and Engineering, 393, 114778.

- Lu, L., Meng, X., Cai, S., Mao, Z., Goswami, S., Zhang, Z., & Karniadakis, G. E. (2022b). *DeepONet: Learning nonlinear operators for nonlinear partial differential equations* [Code Repository]. GitHub. Retrieved from `https://github.com/lu-group/deeponet-fno/tree/main <https://github.com/lu-group/deeponet-fno/tree/main>`_.

- Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021). *DeepXDE: A deep learning library for solving differential equations*. SIAM Review, 63(1), 208-228. doi: `10.1137/19M1274067 <https://doi.org/10.1137/19M1274067>`_.

- NeuralOperator. (2021). *Neural Operator: A comprehensive framework for Fourier neural operators* [Code Repository]. GitHub. Retrieved from `https://github.com/neuraloperator/neuraloperator/tree/main <https://github.com/neuraloperator/neuraloperator/tree/main>`_.

- Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., & Mons, B. (2016). *The FAIR data principles*. Scientific Data, 3(1), 160018. doi: `10.1038/sdata.2016.18 <https://doi.org/10.1038/sdata.2016.18>`_.