@article{casenave2023mmgp,
  title={{MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability}},
  author={Casenave, F. and Staber, B. and Roynard, X.},
  journal={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://arxiv.org/abs/2305.12871},
}

@inproceedings{airfrans,
  author = {Bonnet, F. and Mazari, J. and Cinnella, P. and Gallinari, P.},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
  pages = {23463--23478},
  publisher = {Curran Associates, Inc.},
  title = {{AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier\textendash Stokes Solutions}},
  volume = {35},
  year = {2022},
  url={https://arxiv.org/abs/2212.07564},
}

@misc{casenave2025plaid,
      title={{Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning}},
      author={Casenave, F. and Roynard, X. and Staber, B. and Piat, W. and Bucci, M. A. and Akkari, N. and Kabalan, A. and Nguyen, X. M. V. and Saverio, L. and Carpintero Perez, R. and Kalaydjian, A. and Fouch\'{e}, S. and Gonon, T. and Najjar, G. and Menier, E. and Nastorg, M. and Catalani, G. and Rey, C.},
      year={2025},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.02974},
}
