Nissrine Akkari

16 papers Journal 13Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning.
CoRR
Fabien Casenave, Xavier Roynard, Brian Staber, William Piat, Michele Alessandro Bucci, Nissrine Akkari, Abbas Kabalan, Xuan Minh Vuong Nguyen, Luca Saverio, Raphaël Carpintero Perez, Anthony Kalaydjian, Samy Fouché, Thierry Gonon, Ghassan Najjar, Emmanuel Menier, Matthieu Nastorg, Giovanni Catalani, Christian Rey
2024 — book
Manifold Learning - Model Reduction in Engineering
David Ryckelynck, Fabien Casenave, Nissrine Akkari
2023 J jnl
A priori compression of convolutional neural networks for wave simulators.
CoRR
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck
2023 J jnl
A priori compression of convolutional neural networks for wave simulators.
Eng. Appl. Artif. Intell.
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck
2022 J jnl
An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems.
Adv. Model. Simul. Eng. Sci.
Nissrine Akkari, Fabien Casenave, David Ryckelynck, Christian Rey
2022 J jnl
Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases.
J. Comput. Phys.
Thomas Daniel, Fabien Casenave, Nissrine Akkari, Ali Ketata, David Ryckelynck
2021 J jnl
Data augmentation and feature selection for automatic model recommendation in computational physics.
CoRR
Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck
2021 J jnl
Optimal piecewise linear data compression for solutions of parametrized partial differential equations.
CoRR
Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck
2021 J jnl
Uncertainty quantification for industrial design using dictionaries of reduced order models.
CoRR
Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck, Christian Rey
2021 J jnl
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN.
CoRR
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck
2020 — conf
Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows.
SAI (2)
Nissrine Akkari, Fabien Casenave, Marc-Eric Perrin, David Ryckelynck
2020 J jnl
Model order reduction assisted by deep neural networks (ROM-net).
Adv. Model. Simul. Eng. Sci.
Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck
2020 — conf
Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability.
SAI (2)
Fabien Casenave, Nissrine Akkari, David Ryckelynck
2014 J jnl
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.
Nissrine Akkari, Aziz Hamdouni, Erwan Liberge, Mustapha Jazar
2014 J jnl
Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation.
Appl. Math. Comput.
Nissrine Akkari, Aziz Hamdouni, Mustapha Jazar
2014 J jnl
On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation.
Adv. Model. Simul. Eng. Sci.
Nissrine Akkari, Aziz Hamdouni, Erwan Liberge, Mustapha Jazar