Publications

2023

1. ICML

   Training Deep Surrogate Models with Large Scale Online Learning

   Lucas Meyer, Marc Schouler, Robert Alexander Caulk, and 2 more authors

   In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, Jul 2023

   arXiv Bib PDF

   @inproceedings{meyer2023training,
     author = {Meyer, Lucas and Schouler, Marc and Caulk, Robert Alexander and Rib{\'{e}}s, Alejandro and Raffin, Bruno},
     booktitle = {International Conference on Machine Learning, {ICML} 2023, 23-29 July 2023, Honolulu, Hawaii, {USA}},
     editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
     pages = {24614--24630},
     publisher = {{PMLR}},
     series = {Proceedings of Machine Learning Research},
     title = {{T}raining {D}eep {S}urrogate {M}odels with {L}arge {S}cale {O}nline {L}earning},
     url = {https://proceedings.mlr.press/v202/meyer23b.html},
     volume = {202},
     year = {2023},
     month = jul,
   }

2. SC

   High Throughput Training of Deep Surrogates from Large Ensemble Runs

   Lucas Meyer, Marc Schouler, Robert Alexander Caulk, and 2 more authors

   In Proceedings of the international conference for high performance computing, networking, storage and analysis, Nov 2023

   Bib PDF

   @inproceedings{meyer2023high,
     author = {Meyer, Lucas and Schouler, Marc and Caulk, Robert Alexander and Ribes, Alejandro and Raffin, Bruno},
     booktitle = {Proceedings of the international conference for high performance computing, networking, storage and analysis},
     title = {{H}igh {T}hroughput {T}raining of {D}eep {S}urrogates from {L}arge {E}nsemble {R}uns},
     year = {2023},
     month = nov,
   }

3. JOSS

   Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses

   Marc Schouler, Robert Alexander Caulk, Lucas Meyer, and 9 more authors

   Journal of Open Source Software, Nov 2023

   Bib PDF

   @article{schouler2023,
     author = {Schouler, Marc and Caulk, Robert Alexander and Meyer, Lucas and Terraz, Théophile and Conrads, Christoph and Friedemann, Sebastian and Agarwal, Achal and Baldonado, Juan Manuel and Pogodziński, Bartłomiej and Sekuła, Anna and Ribes, Alejandro and Raffin, Bruno},
     journal = {Journal of Open Source Software},
     title = {Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses},
     doi = {10.21105/joss.05291},
     url = {https://doi.org/10.21105/joss.05291},
     publisher = {The Open Journal},
     volume = {8},
     number = {86},
     pages = {5291},
     year = {2023},
   }

2022

1. ML4Sciences

   Simulation-Based Parallel Training

   Lucas Meyer, Alejandro Ribés, and Bruno Raffin

   Nov 2022

   arXiv Bib PDF

   @article{meyer2022simulation,
     author = {Meyer, Lucas and Rib{\'{e}}s, Alejandro and Raffin, Bruno},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/journals/corr/abs-2211-04119.bib},
     doi = {10.48550/arXiv.2211.04119},
     eprint = {2211.04119},
     eprinttype = {arXiv},
     venue = {NeurIPS 2022 AI for Science Workshop},
     timestamp = {Wed, 16 Aug 2023 01:00:00 +0200},
     title = {{S}imulation-Based {P}arallel {T}raining},
     url = {https://doi.org/10.48550/arXiv.2211.04119},
     volume = {abs/2211.04119},
     year = {2022},
   }

2021

1. ML4Sciences

   Deep Surrogate for Direct Time Fluid Dynamics

   Lucas Meyer, Louen Pottier, Alejandro Ribés, and 1 more author

   Nov 2021

   arXiv Bib PDF

   @article{meyer2021deep,
     author = {Meyer, Lucas and Pottier, Louen and Rib{\'{e}}s, Alejandro and Raffin, Bruno},
     eprint = {2112.10296},
     eprinttype = {arXiv},
     venue = {Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).},
     timestamp = {Wed, 16 Aug 2023 01:00:00 +0200},
     title = {{D}eep {S}urrogate for {D}irect {T}ime {F}luid {D}ynamics},
     url = {https://arxiv.org/abs/2112.10296},
     volume = {abs/2112.10296},
     year = {2021},
   }