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)

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)

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)

Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows. - Nissrine Akkari; Fabien Casenave; Marc-Eric Perrin; David Ryckelynck (SAI (2), 2020)

Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability. - Fabien Casenave; Nissrine Akkari; David Ryckelynck (SAI (2), 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)

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

Optimal piecewise linear data compression for solutions of parametrized partial differential equations. - Thomas Daniel; Fabien Casenave; Nissrine Akkari; David Ryckelynck (CoRR, 2021)

Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN. - Hamza Boukraichi; Nissrine Akkari; Fabien Casenave; David Ryckelynck (CoRR, 2021)

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)

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)

A priori compression of convolutional neural networks for wave simulators. - Hamza Boukraichi; Nissrine Akkari; Fabien Casenave; David Ryckelynck (Eng. Appl. Artif. Intell., 2023)

Manifold Learning - Model Reduction in Engineering - David Ryckelynck; Fabien Casenave; Nissrine Akkari (Springer, 2024)

Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning. - 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; et al. (CoRR, 2025)