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.

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

  • Data Observatory. (n.d.). Data Observatory. Retrieved from https://www.dataobservatory.net/.

  • Docker, Inc. (n.d.). Docker: Enterprise container platform. Retrieved from 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.

  • European Synchrotron Radiation Facility (ESRF). (2024). myhdf5: Online HDF5 file viewing service. Web Application. Retrieved from 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/.

  • 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.

  • Hugging Face. (n.d.). Hugging Face. Retrieved from 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.

  • 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.

  • 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.

  • 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.

  • NeuralOperator. (2021). Neural Operator: A comprehensive framework for Fourier neural operators [Code Repository]. GitHub. Retrieved from 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.