44 citations · 89 across the 9 of their papers we have counts for
9 papers
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
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 (…
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…
An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades
Fabien Casenave, Nissrine Akkari
The industrial application motivating this work is the fatigue computation of aircraft engines' high-pressure turbine blades. The material model involves nonlinear elastoviscoplast…
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…
Nonintrusive approximation of parametrized limits of matrix power algorithms -- application to matrix inverses and log-determinants
Fabien Casenave, Nissrine Akkari, Alexandre Charles +1
We consider in this work quantities that can be obtained as limits of powers of parametrized matrices, for instance the inverse matrix or the logarithm of the determinant. Under th…