44 citations · 135 across the 9 of their papers we have counts for
10 papers
A priori compression of convolutional neural networks for wave simulators
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave +1
Convolutional neural networks are now seeing widespread use in a variety of fields, including image classification, facial and object recognition, medical imaging analysis, and man…
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave +1
The analysis of parametric and non-parametric uncertainties of very large dynamical systems requires the construction of a stochastic model of said system. Linear approaches relyin…
Optimal piecewise linear data compression for solutions of parametrized partial differential equations
Thomas Daniel, Fabien Casenave, Nissrine Akkari +1
Model order reduction has been extensively studied over the last two decades. Projection-based methods such as the Proper Orthogonal Decomposition and the Reduced Basis Method enjo…
Uncertainty quantification for industrial design using dictionaries of reduced order models
Thomas Daniel, Fabien Casenave, Nissrine Akkari +2
We consider the dictionary-based ROM-net (Reduced Order Model) framework [T. Daniel, F. Casenave, N. Akkari, D. Ryckelynck, Model order reduction assisted by deep neural networks (…
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
João P C Bertoldo, Etienne Decencière, David Ryckelynck +1
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome,…
Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases
Thomas Daniel, Fabien Casenave, Nissrine Akkari +2
Nonlinear model order reduction has opened the door to parameter optimization and uncertainty quantification in complex physics problems governed by nonlinear equations. In particu…
Data augmentation and feature selection for automatic model recommendation in computational physics
Thomas Daniel, Fabien Casenave, Nissrine Akkari +1
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the…
Reduced Bond Graph via machine learning for nonlinear multiphysics dynamic systems
Youssef Hammadi, David Ryckelynck, Amin El-Bakkali
We propose a machine learning approach aiming at reducing Bond Graphs. The output of the machine learning is a hybrid modeling that contains a reduced Bond Graph coupled to a simpl…
A Nonintrusive Distributed Reduced Order Modeling Framework for nonlinear structural mechanics -- application to elastoviscoplastic computations
Fabien Casenave, Nissrine Akkari, Felipe Bordeu +2
In this work, we propose a framework that constructs reduced order models for nonlinear structural mechanics in a nonintrusive fashion, and can handle large scale simulations. We i…
An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem
Felix Fritzen, Bernhard Haasdonk, David Ryckelynck +1
A novel algorithmic discussion of the methodological and numerical differences of competing parametric model reduction techniques for nonlinear problems are presented. First, the G…