Model order reduction assisted by deep neural networks (ROM-net) von Daniel, Thomas, Casenave, Fabien, Akkari, Nissrine, Ryckelynck, David

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Manifold Learning: Model Reduction in Engineering von Ryckelynck, David, Casenave, Fabien, Akkari, Nissrine

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An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems von Akkari, Nissrine, Casenave, Fabien, Ryckelynck, David, Rey, Christian

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An Error Indicator-Based Adaptive Reduced Order Model for Nonlinear Structural Mechanics—Application to High-Pressure Turbine Blades von Casenave, Fabien, Akkari, Nissrine

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A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders von Akkari, Nissrine, Casenave, Fabien, Hachem, Elie, Ryckelynck, David

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Time Stable Reduced Order Modeling by an Enhanced Reduced Order Basis of the Turbulent and Incompressible 3D Navier–Stokes Equations von Akkari, Nissrine, Casenave, Fabien, Moureau, Vincent

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Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics von Daniel, Thomas, Casenave, Fabien, Akkari, Nissrine, Ryckelynck, David

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Data-Targeted Prior Distribution for Variational AutoEncoder von Akkari, Nissrine, Casenave, Fabien, Daniel, Thomas, Ryckelynck, David

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Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models von Daniel, Thomas, Casenave, Fabien, Akkari, Nissrine, Ryckelynck, David, Rey, Christian

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Parametrized non intrusive space-time approximation for explicit dynamic fem applications von Boukraichi, Hamza, Razaaly, Nassim, Akkari, Nissrine, Casenave, Fabien, Ryckelynck, David

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Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases von Daniel, Thomas, Casenave, Fabien, Akkari, Nissrine, Ketata, Ali, Ryckelynck, David

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A nonintrusive distributed reduced‐order modeling framework for nonlinear structural mechanics—Application to elastoviscoplastic computations von Casenave, Fabien, Akkari, Nissrine, Bordeu, Felipe, Rey, Christian, Ryckelynck, David

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A priori compression of convolutional neural networks for wave simulators von Boukraichi, Hamza, Akkari, Nissrine, Casenave, Fabien, Ryckelynck, David

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Uncertainty quantification for industrial design using dictionaries of reduced order models von Daniel, Thomas, Casenave, Fabien, Akkari, Nissrine, Ryckelynck, David, Rey, Christian

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Nonintrusive approximation of parametrized limits of matrix power algorithms – application to matrix inverses and log-determinants von Casenave, Fabien, Akkari, Nissrine, Charles, Alexandre, Rey, Christian

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Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametrized Geometrical Variability von Casenave, Fabien, Akkari, Nissrine, Ryckelynck, David

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On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation von Akkari, Nissrine, Hamdouni, Aziz, Liberge, Erwan, Jazar, Mustapha

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Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid Flows von Akkari, Nissrine, Casenave, Fabien, Perrin, Marc-Eric, Ryckelynck, David

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Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN von BOUKRAICHI, Hamza, AKKARI, Nissrine, CASENAVE, Fabien, RYCKELYNCK, David

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An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades von Casenave, Fabien, Akkari, Nissrine

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