Nissrine Akkari

According to our database, Nissrine Akkari authored at least 14 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Manifold Learning - Model Reduction in Engineering

David Ryckelynck, Fabien Casenave, Nissrine Akkari

Springer Briefs in Computer Science, Springer, ISBN: 978-3-031-52766-1, 2024

2023

A priori compression of convolutional neural networks for wave simulators.

Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck

CoRR, 2023

2022

Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases.

Thomas Daniel, Fabien Casenave, Nissrine Akkari, Ali Ketata, David Ryckelynck

J. Comput. Phys., 2022

An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems.

Nissrine Akkari, Fabien Casenave, David Ryckelynck, Christian Rey

Adv. Model. Simul. Eng. Sci., 2022

2021

Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN.

Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck

CoRR, 2021

Optimal piecewise linear data compression for solutions of parametrized partial differential equations.

Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck

CoRR, 2021

Uncertainty quantification for industrial design using dictionaries of reduced order models.

Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck, Christian Rey

CoRR, 2021

Data augmentation and feature selection for automatic model recommendation in computational physics.

Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck

CoRR, 2021

2020

Model order reduction assisted by deep neural networks (ROM-net).

Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck

Adv. Model. Simul. Eng. Sci., 2020

Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability.

Fabien Casenave, Nissrine Akkari, David Ryckelynck

Proceedings of the Intelligent Computing, 2020

Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows.

Nissrine Akkari, Fabien Casenave, Marc-Eric Perrin, David Ryckelynck

Proceedings of the Intelligent Computing, 2020

2014

A mathematical and numerical study of the sensitivity of a reduced order model by POD (ROM-POD), for a 2D incompressible fluid flow.

Nissrine Akkari, Aziz Hamdouni, Erwan Liberge, Mustapha Jazar

J. Comput. Appl. Math., 2014

On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation.

Nissrine Akkari, Aziz Hamdouni, Erwan Liberge, Mustapha Jazar

Adv. Model. Simul. Eng. Sci., 2014

Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation.

Nissrine Akkari, Aziz Hamdouni, Mustapha Jazar

Appl. Math. Comput., 2014