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Nissrine Akkari
Nissrine Akkari
Safran
Verified email at safrangroup.com
Title
Cited by
Cited by
Year
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), 1-27, 2020
352020
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
232014
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
222020
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
182019
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), 1-16, 2014
132014
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
92019
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
72018
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
72017
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
52021
Mathematical study of the sensitivity of the POD method (Proper orthogonal decomposition)
N Akkari
Theses. Université de La Rochelle, 33, 2012
52012
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
42020
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
42014
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
32022
A velocity potential preserving reduced order approach for the incompressible and unsteady Navier-Stokes equations
N Akkari
AIAA Scitech 2020 Forum, 1573, 2020
32020
A novel Gappy reduced order method to capture non-parameterized geometrical variation in fluid dynamics problems
N Akkari, F Casenave, D Ryckelynck
22019
Data-Targeted Prior Distribution for Variational AutoEncoder
N Akkari, F Casenave, T Daniel, D Ryckelynck
Fluids 6 (10), 343, 2021
12021
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
12021
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
12021
Mathematical and numerical results on the parametric sensitivity of a ROM-POD of the burgers equation
N Akkari, A Hamdouni, M Jazar
European Journal of Computational Mechanics 23 (1-2), 78-95, 2014
12014
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), 1-34, 2022
2022
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Articles 1–20