101 citations
- Safran (France)FR10 papers
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8 papers · 1 filter
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 Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search
Kevin Osanlou, Andrei Bursuc, Christophe Guettier +2
Deep learning-based methods are growing prominence for planning purposes. In this paper, we present a hybrid planner that combines a graph machine learning model and an optimal sol…
Learning Deep Morphological Networks with Neural Architecture Search
Yufei Hu, Nacim Belkhir, Jesus Angulo +2
Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for genera…
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects a…
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…
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Clément Bénard, Sébastien da Veiga, Erwan Scornet
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most effi…