FAIR Scientific Machine Learning
This repository is dedicated to advancing the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data in the field of scientific machine learning, particularly focusing on the solutions of partial differential equations (PDEs). It provides well-documented datasets and code that facilitate the reproducible research and collaborative development of PDE-solving algorithms.
If you use this data or code for your research, please cite this GitHub repository:
@misc{fair_sciml2024,
title = {FAIR Scientific Machine Learning},
author = {Paul Escapil, Eduardo Álvarez and Adolfo Parra},
year = {2024},
url = {https://github.com/pescap/fair-sciml}
}