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\n  \n 2025\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Learning large scale industrial physics simulations.\n \n \n \n \n\n\n \n Casenave, F.\n\n\n \n\n\n\n CoRR, abs/2502.08295. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2502-08295,\n  author       = {Fabien Casenave},\n  title        = {Learning large scale industrial physics simulations},\n  journal      = {CoRR},\n  volume       = {abs/2502.08295},\n  year         = {2025},\n  url          = {https://doi.org/10.48550/arXiv.2502.08295},\n  doi          = {10.48550/ARXIV.2502.08295},\n  eprinttype    = {arXiv},\n  eprint       = {2502.08295},\n  timestamp    = {Wed, 12 Mar 2025 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2502-08295.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n O-MMGP: Optimal Mesh Morphing Gaussian Process Regression for Solving PDEs with non-Parametric Geometric Variations.\n \n \n \n \n\n\n \n Kabalan, A.; Casenave, F.; Bordeu, F.; and Ehrlacher, V.\n\n\n \n\n\n\n CoRR, abs/2502.11632. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"O-MMGP:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2502-11632,\n  author       = {Abbas Kabalan and\n                  Fabien Casenave and\n                  Felipe Bordeu and\n                  Virginie Ehrlacher},\n  title        = {{O-MMGP:} Optimal Mesh Morphing Gaussian Process Regression for Solving\n                  PDEs with non-Parametric Geometric Variations},\n  journal      = {CoRR},\n  volume       = {abs/2502.11632},\n  year         = {2025},\n  url          = {https://doi.org/10.48550/arXiv.2502.11632},\n  doi          = {10.48550/ARXIV.2502.11632},\n  eprinttype    = {arXiv},\n  eprint       = {2502.11632},\n  timestamp    = {Wed, 19 Mar 2025 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2502-11632.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning.\n \n \n \n \n\n\n \n Casenave, F.; Roynard, X.; Staber, B.; Piat, W.; Bucci, M. A.; Akkari, N.; Kabalan, A.; Nguyen, X. M. V.; Saverio, L.; Perez, R. C.; Kalaydjian, A.; Fouché, S.; Gonon, T.; Najjar, G.; Menier, E.; Nastorg, M.; Catalani, G.; and Rey, C.\n\n\n \n\n\n\n CoRR, abs/2505.02974. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"Physics-LearningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2505-02974,\n  author       = {Fabien Casenave and\n                  Xavier Roynard and\n                  Brian Staber and\n                  William Piat and\n                  Michele Alessandro Bucci and\n                  Nissrine Akkari and\n                  Abbas Kabalan and\n                  Xuan Minh Vuong Nguyen and\n                  Luca Saverio and\n                  Rapha{\\"{e}}l Carpintero Perez and\n                  Anthony Kalaydjian and\n                  Samy Fouch{\\'{e}} and\n                  Thierry Gonon and\n                  Ghassan Najjar and\n                  Emmanuel Menier and\n                  Matthieu Nastorg and\n                  Giovanni Catalani and\n                  Christian Rey},\n  title        = {Physics-Learning {AI} Datamodel {(PLAID)} datasets: a collection of\n                  physics simulations for machine learning},\n  journal      = {CoRR},\n  volume       = {abs/2505.02974},\n  year         = {2025},\n  url          = {https://doi.org/10.48550/arXiv.2505.02974},\n  doi          = {10.48550/ARXIV.2505.02974},\n  eprinttype    = {arXiv},\n  eprint       = {2505.02974},\n  timestamp    = {Fri, 20 Jun 2025 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2505-02974.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Manifold Learning - Model Reduction in Engineering.\n \n \n \n \n\n\n \n Ryckelynck, D.; Casenave, F.; and Akkari, N.\n\n\n \n\n\n\n of Springer Briefs in Computer ScienceSpringer, 2024.\n \n\n\n\n
\n\n\n\n \n \n \"ManifoldPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@book{DBLP:series/sbcs/RyckelynckCA24,\n  author       = {David Ryckelynck and\n                  Fabien Casenave and\n                  Nissrine Akkari},\n  title        = {Manifold Learning - Model Reduction in Engineering},\n  series       = {Springer Briefs in Computer Science},\n  publisher    = {Springer},\n  year         = {2024},\n  url          = {https://doi.org/10.1007/978-3-031-52764-7},\n  doi          = {10.1007/978-3-031-52764-7},\n  isbn         = {978-3-031-52766-1},\n  timestamp    = {Mon, 01 Apr 2024 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/series/sbcs/RyckelynckCA24.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n An elasticity-based mesh morphing technique with application to reduced-order modeling.\n \n \n \n \n\n\n \n Kabalan, A.; Casenave, F.; Bordeu, F.; Ehrlacher, V.; and Ern, A.\n\n\n \n\n\n\n CoRR, abs/2407.02433. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2407-02433,\n  author       = {Abbas Kabalan and\n                  Fabien Casenave and\n                  Felipe Bordeu and\n                  Virginie Ehrlacher and\n                  Alexandre Ern},\n  title        = {An elasticity-based mesh morphing technique with application to reduced-order\n                  modeling},\n  journal      = {CoRR},\n  volume       = {abs/2407.02433},\n  year         = {2024},\n  url          = {https://doi.org/10.48550/arXiv.2407.02433},\n  doi          = {10.48550/ARXIV.2407.02433},\n  eprinttype    = {arXiv},\n  eprint       = {2407.02433},\n  timestamp    = {Wed, 07 Aug 2024 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2407-02433.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2023\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n A priori compression of convolutional neural networks for wave simulators.\n \n \n \n \n\n\n \n Boukraichi, H.; Akkari, N.; Casenave, F.; and Ryckelynck, D.\n\n\n \n\n\n\n Eng. Appl. Artif. Intell., 126: 106973. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/eaai/BoukraichiACR23,\n  author       = {Hamza Boukraichi and\n                  Nissrine Akkari and\n                  Fabien Casenave and\n                  David Ryckelynck},\n  title        = {A priori compression of convolutional neural networks for wave simulators},\n  journal      = {Eng. Appl. Artif. Intell.},\n  volume       = {126},\n  pages        = {106973},\n  year         = {2023},\n  url          = {https://doi.org/10.1016/j.engappai.2023.106973},\n  doi          = {10.1016/J.ENGAPPAI.2023.106973},\n  timestamp    = {Sat, 04 May 2024 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/eaai/BoukraichiACR23.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n BasicTools: a numerical simulation toolbox.\n \n \n \n \n\n\n \n Bordeu, F.; Casenave, F.; and Cortial, J.\n\n\n \n\n\n\n J. Open Source Softw., 8(86): 5142. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"BasicTools:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/jossw/BordeuCC23,\n  author       = {Felipe Bordeu and\n                  Fabien Casenave and\n                  Julien Cortial},\n  title        = {BasicTools: a numerical simulation toolbox},\n  journal      = {J. Open Source Softw.},\n  volume       = {8},\n  number       = {86},\n  pages        = {5142},\n  year         = {2023},\n  url          = {https://doi.org/10.21105/joss.05142},\n  doi          = {10.21105/JOSS.05142},\n  timestamp    = {Fri, 07 Jul 2023 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/jossw/BordeuCC23.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability.\n \n \n \n \n\n\n \n Casenave, F.; Staber, B.; and Roynard, X.\n\n\n \n\n\n\n In Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M.; and Levine, S., editor(s), Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"MMGP:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{DBLP:conf/nips/CasenaveSR23,\n  author       = {Fabien Casenave and\n                  Brian Staber and\n                  Xavier Roynard},\n  editor       = {Alice Oh and\n                  Tristan Naumann and\n                  Amir Globerson and\n                  Kate Saenko and\n                  Moritz Hardt and\n                  Sergey Levine},\n  title        = {{MMGP:} a Mesh Morphing Gaussian Process-based machine learning method\n                  for regression of physical problems under nonparametrized geometrical\n                  variability},\n  booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference\n                  on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans,\n                  LA, USA, December 10 - 16, 2023},\n  year         = {2023},\n  url          = {http://papers.nips.cc/paper\\_files/paper/2023/hash/89379d5fc6eb34ff98488202fb52b9d0-Abstract-Conference.html},\n  timestamp    = {Fri, 01 Mar 2024 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/conf/nips/CasenaveSR23.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A priori compression of convolutional neural networks for wave simulators.\n \n \n \n \n\n\n \n Boukraichi, H.; Akkari, N.; Casenave, F.; and Ryckelynck, D.\n\n\n \n\n\n\n CoRR, abs/2304.04964. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2304-04964,\n  author       = {Hamza Boukraichi and\n                  Nissrine Akkari and\n                  Fabien Casenave and\n                  David Ryckelynck},\n  title        = {A priori compression of convolutional neural networks for wave simulators},\n  journal      = {CoRR},\n  volume       = {abs/2304.04964},\n  year         = {2023},\n  url          = {https://doi.org/10.48550/arXiv.2304.04964},\n  doi          = {10.48550/ARXIV.2304.04964},\n  eprinttype    = {arXiv},\n  eprint       = {2304.04964},\n  timestamp    = {Wed, 19 Apr 2023 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2304-04964.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability.\n \n \n \n \n\n\n \n Casenave, F.; Staber, B.; and Roynard, X.\n\n\n \n\n\n\n CoRR, abs/2305.12871. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"MMGP:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/corr/abs-2305-12871,\n  author       = {Fabien Casenave and\n                  Brian Staber and\n                  Xavier Roynard},\n  title        = {{MMGP:} a Mesh Morphing Gaussian Process-based machine learning method\n                  for regression of physical problems under non-parameterized geometrical\n                  variability},\n  journal      = {CoRR},\n  volume       = {abs/2305.12871},\n  year         = {2023},\n  url          = {https://doi.org/10.48550/arXiv.2305.12871},\n  doi          = {10.48550/ARXIV.2305.12871},\n  eprinttype    = {arXiv},\n  eprint       = {2305.12871},\n  timestamp    = {Fri, 26 May 2023 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-12871.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems.\n \n \n \n \n\n\n \n Akkari, N.; Casenave, F.; Ryckelynck, D.; and Rey, C.\n\n\n \n\n\n\n Adv. Model. Simul. Eng. Sci., 9(1): 3. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DBLP:journals/amses/AkkariCRR22,\n  author       = {Nissrine Akkari and\n                  Fabien Casenave and\n                  David Ryckelynck and\n                  Christian Rey},\n  title        = {An updated Gappy-POD to capture non-parameterized geometrical variation\n                  in fluid dynamics problems},\n  journal      = {Adv. Model. Simul. Eng. Sci.},\n  volume       = {9},\n  number       = {1},\n  pages        = {3},\n  year         = {2022},\n  url          = {https://doi.org/10.1186/s40323-022-00215-x},\n  doi          = {10.1186/S40323-022-00215-X},\n  timestamp    = {Mon, 28 Aug 2023 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/amses/AkkariCRR22.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases.\n \n \n \n \n\n\n \n Daniel, T.; Casenave, F.; Akkari, N.; Ketata, A.; and Ryckelynck, D.\n\n\n \n\n\n\n J. Comput. Phys., 458: 111120. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Physics-informedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/jcphy/DanielCAKR22,\n  author       = {Thomas Daniel and\n                  Fabien Casenave and\n                  Nissrine Akkari and\n                  Ali Ketata and\n                  David Ryckelynck},\n  title        = {Physics-informed cluster analysis and a priori efficiency criterion\n                  for the construction of local reduced-order bases},\n  journal      = {J. Comput. Phys.},\n  volume       = {458},\n  pages        = {111120},\n  year         = {2022},\n  url          = {https://doi.org/10.1016/j.jcp.2022.111120},\n  doi          = {10.1016/J.JCP.2022.111120},\n  timestamp    = {Wed, 07 Dec 2022 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/jcphy/DanielCAKR22.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2021\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Data augmentation and feature selection for automatic model recommendation in computational physics.\n \n \n \n \n\n\n \n Daniel, T.; Casenave, F.; Akkari, N.; and Ryckelynck, D.\n\n\n \n\n\n\n CoRR, abs/2101.04530. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"DataPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2101-04530,\n  author       = {Thomas Daniel and\n                  Fabien Casenave and\n                  Nissrine Akkari and\n                  David Ryckelynck},\n  title        = {Data augmentation and feature selection for automatic model recommendation\n                  in computational physics},\n  journal      = {CoRR},\n  volume       = {abs/2101.04530},\n  year         = {2021},\n  url          = {https://arxiv.org/abs/2101.04530},\n  eprinttype    = {arXiv},\n  eprint       = {2101.04530},\n  timestamp    = {Tue, 26 Jan 2021 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2101-04530.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases.\n \n \n \n \n\n\n \n Daniel, T.; Ketata, A.; Casenave, F.; and Ryckelynck, D.\n\n\n \n\n\n\n CoRR, abs/2103.13683. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Physics-informedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2103-13683,\n  author       = {Thomas Daniel and\n                  Ali Ketata and\n                  Fabien Casenave and\n                  David Ryckelynck},\n  title        = {Physics-informed cluster analysis and a priori efficiency criterion\n                  for the construction of local reduced-order bases},\n  journal      = {CoRR},\n  volume       = {abs/2103.13683},\n  year         = {2021},\n  url          = {https://arxiv.org/abs/2103.13683},\n  eprinttype    = {arXiv},\n  eprint       = {2103.13683},\n  timestamp    = {Wed, 07 Apr 2021 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2103-13683.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Uncertainty quantification for industrial design using dictionaries of reduced order models.\n \n \n \n \n\n\n \n Daniel, T.; Casenave, F.; Akkari, N.; Ryckelynck, D.; and Rey, C.\n\n\n \n\n\n\n CoRR, abs/2108.04012. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"UncertaintyPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2108-04012,\n  author       = {Thomas Daniel and\n                  Fabien Casenave and\n                  Nissrine Akkari and\n                  David Ryckelynck and\n                  Christian Rey},\n  title        = {Uncertainty quantification for industrial design using dictionaries\n                  of reduced order models},\n  journal      = {CoRR},\n  volume       = {abs/2108.04012},\n  year         = {2021},\n  url          = {https://arxiv.org/abs/2108.04012},\n  eprinttype    = {arXiv},\n  eprint       = {2108.04012},\n  timestamp    = {Wed, 11 Aug 2021 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2108-04012.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Optimal piecewise linear data compression for solutions of parametrized partial differential equations.\n \n \n \n \n\n\n \n Daniel, T.; Casenave, F.; Akkari, N.; and Ryckelynck, D.\n\n\n \n\n\n\n CoRR, abs/2108.12291. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"OptimalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2108-12291,\n  author       = {Thomas Daniel and\n                  Fabien Casenave and\n                  Nissrine Akkari and\n                  David Ryckelynck},\n  title        = {Optimal piecewise linear data compression for solutions of parametrized\n                  partial differential equations},\n  journal      = {CoRR},\n  volume       = {abs/2108.12291},\n  year         = {2021},\n  url          = {https://arxiv.org/abs/2108.12291},\n  eprinttype    = {arXiv},\n  eprint       = {2108.12291},\n  timestamp    = {Thu, 02 Sep 2021 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2108-12291.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN.\n \n \n \n \n\n\n \n Boukraichi, H.; Akkari, N.; Casenave, F.; and Ryckelynck, D.\n\n\n \n\n\n\n CoRR, abs/2110.13680. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"UncertaintyPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2110-13680,\n  author       = {Hamza Boukraichi and\n                  Nissrine Akkari and\n                  Fabien Casenave and\n                  David Ryckelynck},\n  title        = {Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN},\n  journal      = {CoRR},\n  volume       = {abs/2110.13680},\n  year         = {2021},\n  url          = {https://arxiv.org/abs/2110.13680},\n  eprinttype    = {arXiv},\n  eprint       = {2110.13680},\n  timestamp    = {Fri, 29 Oct 2021 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2110-13680.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2020\n \n \n (4)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n \n Model order reduction assisted by deep neural networks (ROM-net).\n \n \n \n \n\n\n \n Daniel, T.; Casenave, F.; Akkari, N.; and Ryckelynck, D.\n\n\n \n\n\n\n Adv. Model. Simul. Eng. Sci., 7(1): 16. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ModelPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/amses/DanielCAR20,\n  author       = {Thomas Daniel and\n                  Fabien Casenave and\n                  Nissrine Akkari and\n                  David Ryckelynck},\n  title        = {Model order reduction assisted by deep neural networks (ROM-net)},\n  journal      = {Adv. Model. Simul. Eng. Sci.},\n  volume       = {7},\n  number       = {1},\n  pages        = {16},\n  year         = {2020},\n  url          = {https://doi.org/10.1186/s40323-020-00153-6},\n  doi          = {10.1186/S40323-020-00153-6},\n  timestamp    = {Sat, 05 Sep 2020 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/amses/DanielCAR20.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A nonintrusive reduced order model for nonlinear transient thermal problems with nonparametrized variability.\n \n \n \n \n\n\n \n Casenave, F.; Gariah, A.; Rey, C.; and Feyel, F.\n\n\n \n\n\n\n Adv. Model. Simul. Eng. Sci., 7(1): 22. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/amses/CasenaveGRF20,\n  author       = {Fabien Casenave and\n                  Asven Gariah and\n                  Christian Rey and\n                  Fr{\\'{e}}d{\\'{e}}ric Feyel},\n  title        = {A nonintrusive reduced order model for nonlinear transient thermal\n                  problems with nonparametrized variability},\n  journal      = {Adv. Model. Simul. Eng. Sci.},\n  volume       = {7},\n  number       = {1},\n  pages        = {22},\n  year         = {2020},\n  url          = {https://doi.org/10.1186/s40323-020-00156-3},\n  doi          = {10.1186/S40323-020-00156-3},\n  timestamp    = {Fri, 05 Jul 2024 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/amses/CasenaveGRF20.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability.\n \n \n \n \n\n\n \n Casenave, F.; Akkari, N.; and Ryckelynck, D.\n\n\n \n\n\n\n In Arai, K.; Kapoor, S.; and Bhatia, R., editor(s), Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 2, AI 2020, London, UK, 16-17 July 2020, volume 1229, of Advances in Intelligent Systems and Computing, pages 245–263, 2020. Springer\n \n\n\n\n
\n\n\n\n \n \n \"ReducedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{DBLP:conf/sai/CasenaveAR20,\n  author       = {Fabien Casenave and\n                  Nissrine Akkari and\n                  David Ryckelynck},\n  editor       = {Kohei Arai and\n                  Supriya Kapoor and\n                  Rahul Bhatia},\n  title        = {Reduced Order Modeling Assisted by Convolutional Neural Network for\n                  Thermal Problems with Nonparametrized Geometrical Variability},\n  booktitle    = {Intelligent Computing - Proceedings of the 2020 Computing Conference,\n                  Volume 2, {AI} 2020, London, UK, 16-17 July 2020},\n  series       = {Advances in Intelligent Systems and Computing},\n  volume       = {1229},\n  pages        = {245--263},\n  publisher    = {Springer},\n  year         = {2020},\n  url          = {https://doi.org/10.1007/978-3-030-52246-9\\_17},\n  doi          = {10.1007/978-3-030-52246-9\\_17},\n  timestamp    = {Tue, 07 Jul 2020 15:55:45 +0200},\n  biburl       = {https://dblp.org/rec/conf/sai/CasenaveAR20.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows.\n \n \n \n \n\n\n \n Akkari, N.; Casenave, F.; Perrin, M.; and Ryckelynck, D.\n\n\n \n\n\n\n In Arai, K.; Kapoor, S.; and Bhatia, R., editor(s), Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 2, AI 2020, London, UK, 16-17 July 2020, volume 1229, of Advances in Intelligent Systems and Computing, pages 264–276, 2020. Springer\n \n\n\n\n
\n\n\n\n \n \n \"DeepPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{DBLP:conf/sai/AkkariCPR20,\n  author       = {Nissrine Akkari and\n                  Fabien Casenave and\n                  Marc{-}Eric Perrin and\n                  David Ryckelynck},\n  editor       = {Kohei Arai and\n                  Supriya Kapoor and\n                  Rahul Bhatia},\n  title        = {Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible\n                  and Unsteady Fluid Flows},\n  booktitle    = {Intelligent Computing - Proceedings of the 2020 Computing Conference,\n                  Volume 2, {AI} 2020, London, UK, 16-17 July 2020},\n  series       = {Advances in Intelligent Systems and Computing},\n  volume       = {1229},\n  pages        = {264--276},\n  publisher    = {Springer},\n  year         = {2020},\n  url          = {https://doi.org/10.1007/978-3-030-52246-9\\_18},\n  doi          = {10.1007/978-3-030-52246-9\\_18},\n  timestamp    = {Tue, 07 Jul 2020 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/conf/sai/AkkariCPR20.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2016\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Variants of the Empirical Interpolation Method: Symmetric formulation, choice of norms and rectangular extension.\n \n \n \n \n\n\n \n Casenave, F.; Ern, A.; and Lelièvre, T.\n\n\n \n\n\n\n Appl. Math. Lett., 56: 23–28. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"VariantsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/appml/CasenaveEL16,\n  author       = {Fabien Casenave and\n                  Alexandre Ern and\n                  Tony Leli{\\`{e}}vre},\n  title        = {Variants of the Empirical Interpolation Method: Symmetric formulation,\n                  choice of norms and rectangular extension},\n  journal      = {Appl. Math. Lett.},\n  volume       = {56},\n  pages        = {23--28},\n  year         = {2016},\n  url          = {https://doi.org/10.1016/j.aml.2015.11.010},\n  doi          = {10.1016/J.AML.2015.11.010},\n  timestamp    = {Mon, 26 Oct 2020 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/appml/CasenaveEL16.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2015\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n A nonintrusive reduced basis method applied to aeroacoustic simulations.\n \n \n \n \n\n\n \n Casenave, F.; Ern, A.; and Lelièvre, T.\n\n\n \n\n\n\n Adv. Comput. Math., 41(5): 961–986. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/adcm/CasenaveEL15,\n  author       = {Fabien Casenave and\n                  Alexandre Ern and\n                  Tony Leli{\\`{e}}vre},\n  title        = {A nonintrusive reduced basis method applied to aeroacoustic simulations},\n  journal      = {Adv. Comput. Math.},\n  volume       = {41},\n  number       = {5},\n  pages        = {961--986},\n  year         = {2015},\n  url          = {https://doi.org/10.1007/s10444-014-9365-0},\n  doi          = {10.1007/S10444-014-9365-0},\n  timestamp    = {Mon, 26 Oct 2020 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/adcm/CasenaveEL15.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Boundary element and finite element coupling for aeroacoustics simulations.\n \n \n \n \n\n\n \n Balin, N.; Casenave, F.; Dubois, F.; Duceau, E.; Duprey, S.; and Terrasse, I.\n\n\n \n\n\n\n J. Comput. Phys., 294: 274–296. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"BoundaryPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/jcphy/BalinCDDDT15,\n  author       = {Nolwenn Balin and\n                  Fabien Casenave and\n                  Fran{\\c{c}}ois Dubois and\n                  Eric Duceau and\n                  Stefan Duprey and\n                  Isabelle Terrasse},\n  title        = {Boundary element and finite element coupling for aeroacoustics simulations},\n  journal      = {J. Comput. Phys.},\n  volume       = {294},\n  pages        = {274--296},\n  year         = {2015},\n  url          = {https://doi.org/10.1016/j.jcp.2015.03.044},\n  doi          = {10.1016/J.JCP.2015.03.044},\n  timestamp    = {Wed, 19 Feb 2020 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/jcphy/BalinCDDDT15.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2014\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Coupled BEM-FEM for the convected Helmholtz equation with non-uniform flow in a bounded domain.\n \n \n \n \n\n\n \n Casenave, F.; Ern, A.; and Sylvand, G.\n\n\n \n\n\n\n J. Comput. Phys., 257: 627–644. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"CoupledPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/jcphy/CasenaveES14,\n  author       = {Fabien Casenave and\n                  Alexandre Ern and\n                  Guillaume Sylvand},\n  title        = {Coupled {BEM-FEM} for the convected Helmholtz equation with non-uniform\n                  flow in a bounded domain},\n  journal      = {J. Comput. Phys.},\n  volume       = {257},\n  pages        = {627--644},\n  year         = {2014},\n  url          = {https://doi.org/10.1016/j.jcp.2013.10.016},\n  doi          = {10.1016/J.JCP.2013.10.016},\n  timestamp    = {Wed, 19 Feb 2020 00:00:00 +0100},\n  biburl       = {https://dblp.org/rec/journals/jcphy/CasenaveES14.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n An Empirical Interpolation based Fast Summation Method for translation invariant kernels.\n \n \n \n \n\n\n \n Casenave, F.\n\n\n \n\n\n\n CoRR, abs/1408.0210. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/Casenave14,\n  author       = {Fabien Casenave},\n  title        = {An Empirical Interpolation based Fast Summation Method for translation\n                  invariant kernels},\n  journal      = {CoRR},\n  volume       = {abs/1408.0210},\n  year         = {2014},\n  url          = {http://arxiv.org/abs/1408.0210},\n  eprinttype    = {arXiv},\n  eprint       = {1408.0210},\n  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/Casenave14.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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