@article{casenave2025physics,
  title={Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning},
  author={Casenave, Fabien and Roynard, Xavier and Staber, Brian and Akkari, Nissrine and Piat, William and Bucci, Michele Alessandro and Kabalan, Abbas and Nguyen, Xuan Minh Vuong and Saverio, Luca and Perez, Rapha{\"e}l Carpintero and others},
  journal={arXiv preprint arXiv:2505.02974},
  year={2025},
  doi={10.48550/arXiv.2505.02974}
}

@inproceedings{poinot2018seven,
  title={Seven keys for practical understanding and use of CGNS},
  author={Poinot, Marc and Rumsey, Christopher L},
  booktitle={2018 AIAA Aerospace Sciences Meeting},
  pages={1503},
  year={2018},
  doi={10.2514/6.2018-1503}
}

@article{casenave2024mmgp,
  title={{MMGP}: a {M}esh {M}orphing {G}aussian {P}rocess-based machine learning method for regression of physical problems under nonparametrized geometrical variability},
  author={Casenave, Fabien and Staber, Brian and Roynard, Xavier},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

@article{kabalan2025elasticity,
  title={Elasticity-based morphing technique and application to reduced-order modeling},
  author={Kabalan, Abbas and Casenave, Fabien and Bordeu, Felipe and Ehrlacher, Virginie and Ern, Alexandre},
  journal={Applied Mathematical Modelling},
  volume={141},
  pages={115929},
  year={2025},
  publisher={Elsevier},
  doi={10.1016/j.apm.2025.115929}
}

@article{kabalan2025ommgp,
  title={{O-MMGP}: {O}ptimal {M}esh {M}orphing {G}aussian {P}rocess Regression for Solving {PDEs} with non-Parametric Geometric Variations},
  author={Kabalan, Abbas and Casenave, Fabien and Bordeu, Felipe and Ehrlacher, Virginie},
  journal={arXiv preprint arXiv:2502.11632},
  year={2025},
  doi={10.48550/arXiv.2502.11632},
}

@inproceedings{perez2024gaussian,
  title={{Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels}},
  author={Carpintero Perez, Rapha{\"e}l and Da Veiga, S{\'e}bastien and Garnier, Josselin and Staber, Brian},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1297--1305},
  year={2024},
  organization={PMLR}
}

@article{perez2024learning,
  title={{Learning signals defined on graphs with optimal transport and Gaussian process regression}},
  author={Carpintero Perez, Rapha{\"e}l and Da Veiga, S{\'e}bastien and Garnier, Josselin and Staber, Brian},
  journal={arXiv preprint arXiv:2410.15721},
  year={2024},
  doi={10.48550/arXiv.2410.15721},
}

@article{ohana2024well,
  title={The well: a large-scale collection of diverse physics simulations for machine learning},
  author={Ohana, Ruben and McCabe, Michael and Meyer, Lucas and Morel, Rudy and Agocs, Fruzsina and Beneitez, Miguel and Berger, Marsha and Burkhart, Blakesly and Dalziel, Stuart and Fielding, Drummond and others},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={44989--45037},
  year={2024}
}

@article{pdebench,
  title={Pdebench: An extensive benchmark for scientific machine learning},
  author={Takamoto, Makoto and Praditia, Timothy and Leiteritz, Raphael and MacKinlay, Daniel and Alesiani, Francesco and Pfl{\"u}ger, Dirk and Niepert, Mathias},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={1596--1611},
  year={2022}
}

@article{pdearena,
  title={Towards multi-spatiotemporal-scale generalized pde modeling},
  author={Gupta, Jayesh K and Brandstetter, Johannes},
  journal={arXiv preprint arXiv:2209.15616},
  year={2022}
}