| Model order reduction assisted by deep neural networks (ROM-net) T Daniel, F Casenave, N Akkari, D Ryckelynck Advanced Modeling and Simulation in Engineering Sciences 7, 1-27, 2020 | 110 | 2020 |
| Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases T Daniel, F Casenave, N Akkari, A Ketata, D Ryckelynck Journal of Computational Physics 458, 111120, 2022 | 26 | 2022 |
| Data augmentation and feature selection for automatic model recommendation in computational physics T Daniel, F Casenave, N Akkari, D Ryckelynck Mathematical and Computational Applications 26 (1), 17, 2021 | 18 | 2021 |
| Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models T Daniel, F Casenave, N Akkari, D Ryckelynck, C Rey Mechanics & Industry 23, 3, 2022 | 14 | 2022 |
| Data-Targeted Prior Distribution for Variational AutoEncoder N Akkari, F Casenave, T Daniel, D Ryckelynck Fluids 6 (10), 343, 2021 | 7 | 2021 |
| Machine learning for nonlinear model order reduction T Daniel Université Paris sciences et lettres, 2021 | 4 | 2021 |
| Optimal piecewise linear data compression for solutions of parametrized partial differential equations T Daniel, F Casenave, N Akkari, D Ryckelynck arXiv preprint arXiv:2108.12291, 2021 | 2 | 2021 |
| Uncertainty quantification for industrial design using dictionaries of reduced order models T Daniel, F Casenave, N Akkari, D Ryckelynck, C Rey arXiv preprint arXiv:2108.04012, 2021 | | 2021 |
Nissrine AkkariSafranVahvistettu sähköpostiosoite verkkotunnuksessa safrangroup.com