An Empirical Interpolation based Fast Summation Method for translation invariant kernels. - Fabien Casenave (CoRR, 2014)

Coupled BEM-FEM for the convected Helmholtz equation with non-uniform flow in a bounded domain. - Fabien Casenave; Alexandre Ern; Guillaume Sylvand (J. Comput. Phys., 2014)

A nonintrusive reduced basis method applied to aeroacoustic simulations. - Fabien Casenave; Alexandre Ern; Tony Lelièvre (Adv. Comput. Math., 2015)

Boundary element and finite element coupling for aeroacoustics simulations. - Nolwenn Balin; Fabien Casenave; François Dubois; Eric Duceau; Stefan Duprey; Isabelle Terrasse (J. Comput. Phys., 2015)

Variants of the Empirical Interpolation Method: Symmetric formulation, choice of norms and rectangular extension. - Fabien Casenave; Alexandre Ern; Tony Lelièvre (Appl. Math. Lett., 2016)

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)

A nonintrusive reduced order model for nonlinear transient thermal problems with nonparametrized variability. - Fabien Casenave; Asven Gariah; Christian Rey; Frédéric Feyel (Adv. Model. Simul. Eng. Sci., 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)

Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases. - Thomas Daniel; Ali Ketata; Fabien Casenave; 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)

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability. - Fabien Casenave; Brian Staber; Xavier Roynard (NeurIPS, 2023)

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability. - Fabien Casenave; Brian Staber; Xavier Roynard (CoRR, 2023)

BasicTools: a numerical simulation toolbox. - Felipe Bordeu; Fabien Casenave; Julien Cortial (J. Open Source Softw., 2023)

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

An elasticity-based mesh morphing technique with application to reduced-order modeling. - Abbas Kabalan; Fabien Casenave; Felipe Bordeu; Virginie Ehrlacher; Alexandre Ern (CoRR, 2024)

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

Learning large scale industrial physics simulations. - Fabien Casenave (CoRR, 2025)

O-MMGP: Optimal Mesh Morphing Gaussian Process Regression for Solving PDEs with non-Parametric Geometric Variations. - Abbas Kabalan; Fabien Casenave; Felipe Bordeu; Virginie Ehrlacher (CoRR, 2025)

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)

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis. - Mouadh Yagoubi; David Danan; Milad Leyli-Abadi; Jocelyn Ahmed Mazari; Jean-Patrick Brunet; Abbas Kabalan; Fabien Casenave; Yuxin Ma; Giovanni Catalani; Jean Fesquet; Jacob Helwig; Xuan Zhang; et al. (CoRR, 2025)