44 citations · 294 across the 20 of their papers we have counts for
20 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…
Elasticity-based morphing technique and application to reduced-order modeling
Abbas Kabalan, Fabien Casenave, Felipe Bordeu +2
The aim of this article is to introduce a new methodology for constructing morphings between shapes that have identical topology. The morphings are obtained by deforming a referenc…
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
When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effect…
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 (…
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…
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…
Variants of the Empirical Interpolation Method: symmetric formulation, choice of norms and rectangular extension
Fabien Casenave, Alexandre Ern, Tony Lelièvre
The Empirical Interpolation Method (EIM) is a greedy procedure that constructs approximate representations of two-variable functions in separated form. In its classical presentatio…
A Fast Summation Method for translation invariant kernels
Fabien Casenave
We derive a Fast Multipole Method (FMM) where a low-rank approximation of the kernel is obtained using the Empirical Interpolation Method (EIM). Contrary to classical interpolation…
Boundary Element and Finite Element Coupling for Aeroacoustics Simulations
Nolwenn Balin, Fabien Casenave, François Dubois +3
We consider the scattering of acoustic perturbations in a presence of a flow. We suppose that the space can be split into a zone where the flow is uniform and a zone where the flow…
A nonintrusive Reduced Basis Method applied to aeroacoustic simulations
Fabien Casenave, Alexandre Ern, Tony Lelièvre
The Reduced Basis Method can be exploited in an efficient way only if the so-called affine dependence assumption on the operator and right-hand side of the considered problem with…
A nonintrusive method to approximate linear systems with nonlinear parameter dependence
Fabien Casenave, Alexandre Ern, Tony Lelièvre +1
We consider a family of linear systems with system matrix depending on a parameter and for simplicity parameter-independent right-hand side . These linear sys…
Coupled BEM-FEM for the convected Helmholtz equation with non-uniform flow in a bounded domain
Fabien Casenave, Alexandre Ern, Guillaume Sylvand
We consider the convected Helmholtz equation modeling linear acoustic propagation at a fixed frequency in a subsonic flow around a scattering object. The flow is supposed to be uni…
Accurate and efficient evaluation of the a posteriori error estimator in the reduced basis method
Fabien Casenave, Alexandre Ern, Tony Lelièvre
The reduced basis method is a model reduction technique yielding substantial savings of computational time when a solution to a parametrized equation has to be computed for many va…
Accurate a posteriori error evaluation in the reduced basis method
Fabien Casenave
In the reduced basis method, the evaluation of the a posteriori estimator can become very sensitive to round-off errors. In this note, the origin of the loss of accuracy is reveale…