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Nissrine Akkari
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2020 – today
- 2024
- [b1]David Ryckelynck
, Fabien Casenave, Nissrine Akkari:
Manifold Learning - Model Reduction in Engineering. Springer Briefs in Computer Science, Springer 2024, ISBN 978-3-031-52766-1, pp. 1-107 - 2023
- [j7]Hamza Boukraichi
A priori compression of convolutional neural networks for wave simulators. Eng. Appl. Artif. Intell. 126: 106973 (2023) - [i5]Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck:
A priori compression of convolutional neural networks for wave simulators. CoRR abs/2304.04964 (2023) - 2022
- [j6]Nissrine Akkari, Fabien Casenave, David Ryckelynck
An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems. Adv. Model. Simul. Eng. Sci. 9(1): 3 (2022) - [j5]Thomas Daniel
Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases. J. Comput. Phys. 458: 111120 (2022) - 2021
- [i4]Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck:
Data augmentation and feature selection for automatic model recommendation in computational physics. CoRR abs/2101.04530 (2021) - [i3]Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck, Christian Rey:
Uncertainty quantification for industrial design using dictionaries of reduced order models. CoRR abs/2108.04012 (2021) - [i2]Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck:
Optimal piecewise linear data compression for solutions of parametrized partial differential equations. CoRR abs/2108.12291 (2021) - [i1]Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck:
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN. CoRR abs/2110.13680 (2021) - 2020
- [j4]Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck
Model order reduction assisted by deep neural networks (ROM-net). Adv. Model. Simul. Eng. Sci. 7(1): 16 (2020) - [c2]Fabien Casenave, Nissrine Akkari, David Ryckelynck:
Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability. SAI (2) 2020: 245-263 - [c1]Nissrine Akkari, Fabien Casenave, Marc-Eric Perrin, David Ryckelynck:
Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows. SAI (2) 2020: 264-276
2010 – 2019
- 2014
- [j3]Nissrine Akkari, Aziz Hamdouni, Mustapha Jazar:
Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation. Appl. Math. Comput. 247: 951-961 (2014) - [j2]Nissrine Akkari, Aziz Hamdouni, Erwan Liberge
On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation. Adv. Model. Simul. Eng. Sci. 1(1): 14:1-14:16 (2014) - [j1]Nissrine Akkari, Aziz Hamdouni
A mathematical and numerical study of the sensitivity of a reduced order model by POD (ROM-POD), for a 2D incompressible fluid flow. J. Comput. Appl. Math. 270: 522-530 (2014)
Coauthor Index
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