| 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 | 101 | 2020 |
| A nonintrusive distributed reduced‐order modeling framework for nonlinear structural mechanics—Application to elastoviscoplastic computations F Casenave, N Akkari, F Bordeu, C Rey, D Ryckelynck International journal for numerical methods in engineering 121 (1), 32-53, 2020 | 42 | 2020 |
| Time stable reduced order modeling by an enhanced reduced order basis of the turbulent and incompressible 3D Navier–Stokes equations N Akkari, F Casenave, V Moureau Mathematical and computational applications 24 (2), 45, 2019 | 29 | 2019 |
| A mathematical and numerical study of the sensitivity of a reduced order model by POD (ROM–POD), for a 2D incompressible fluid flow N Akkari, A Hamdouni, E Liberge, M Jazar Journal of Computational and Applied Mathematics 270, 522-530, 2014 | 28 | 2014 |
| 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 | 24 | 2022 |
| On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation N Akkari, A Hamdouni, E Liberge, M Jazar Advanced Modeling and Simulation in Engineering Sciences 1, 1-16, 2014 | 17 | 2014 |
| 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 | 14 | 2021 |
| An error indicator-based adaptive reduced order model for nonlinear structural mechanics—application to high-pressure turbine blades F Casenave, N Akkari Mathematical and computational applications 24 (2), 41, 2019 | 13 | 2019 |
| A bayesian nonlinear reduced order modeling using variational autoencoders N Akkari, F Casenave, E Hachem, D Ryckelynck Fluids 7 (10), 334, 2022 | 12 | 2022 |
| Stable pod-galerkin reduced order models for unsteady turbulent incompressible flows N Akkari, R Mercier, G Lartigue, V Moureau 55th AIAA Aerospace Sciences Meeting, 1000, 2017 | 11 | 2017 |
| 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 | 9 | 2022 |
| Geometrical reduced order modeling (ROM) by proper orthogonal decomposition (POD) for the incompressible Navier Stokes equations N Akkari, R Mercier, V Moureau 2018 AIAA Aerospace Sciences Meeting, 1827, 2018 | 8 | 2018 |
| Deep convolutional generative adversarial networks applied to 2D incompressible and unsteady fluid flows N Akkari, F Casenave, ME Perrin, D Ryckelynck Science and Information Conference, 264-276, 2020 | 7 | 2020 |
| Data-targeted prior distribution for variational autoencoder N Akkari, F Casenave, T Daniel, D Ryckelynck Fluids 6 (10), 343, 2021 | 6 | 2021 |
| An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems N Akkari, F Casenave, D Ryckelynck, C Rey Advanced Modeling and Simulation in Engineering Sciences 9 (1), 3, 2022 | 5 | 2022 |
| Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation N Akkari, A Hamdouni, M Jazar Applied Mathematics and Computation 247, 951-961, 2014 | 5 | 2014 |
| Mathematical study of the sensitivity of the POD method (Proper orthogonal decomposition) N Akkari Theses, Université de La Rochelle, 2012 | 5 | 2012 |
| A priori compression of convolutional neural networks for wave simulators H Boukraichi, N Akkari, F Casenave, D Ryckelynck Engineering Applications of Artificial Intelligence 126, 106973, 2023 | 4 | 2023 |
| A velocity potential preserving reduced order approach for the incompressible and unsteady Navier-Stokes equations N Akkari AIAA Scitech 2020 Forum, 1573, 2020 | 4 | 2020 |
| Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN H Boukraichi, N Akkari, F Casenave, D Ryckelynck IFAC-PapersOnLine 55 (20), 469-474, 2022 | 3 | 2022 |
