A priori reduction method for solving the two-dimensional Burgers' equations. - Cyrille Allery; Aziz Hamdouni; David Ryckelynck; N. Verdon (Appl. Math. Comput., 2011)

Estimation of the validity domain of hyper-reduction approximations in generalized standard elastoviscoplasticity. - David Ryckelynck; Laurent Gallimard; Samuel Jules (Adv. Model. Simul. Eng. Sci., 2015)

Hyper-reduction framework for model calibration in plasticity-induced fatigue. - David Ryckelynck; Djamel Missoum Benziane (Adv. Model. Simul. Eng. Sci., 2016)

An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem. - Felix Fritzen; Bernard Haasdonk; David Ryckelynck; Sebastian Schöps (CoRR, 2016)

Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests. - Franck Nguyen; Selim M. Barhli; Daniel Pino Muñoz; David Ryckelynck (Complex., 2018)

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)

Reduced Bond Graph via machine learning for nonlinear multiphysics dynamic systems. - Youssef Hammadi; David Ryckelynck; Amin El-Bakkali (CoRR, 2020)

Data-driven reduced bond graph for nonlinear multiphysics dynamic systems. - Youssef Hammadi; David Ryckelynck; Amin El-Bakkali (Appl. Math. Comput., 2021)

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)

A modular U-Net for automated segmentation of X-ray tomography images in composite materials. - João P. C. Bertoldo; Etienne Decencière; David Ryckelynck; Henry Proudhon (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 Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers. - Matteo Bastico; David Ryckelynck; Laurent Corté; Yannick Tillier; Etienne Decencière (ICCV (Workshops), 2023)

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

Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching. - Matteo Bastico; Etienne Decencière; Laurent Corté; Yannick Tillier; David Ryckelynck (CVPR, 2024)

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