34 citations · 64 across the 8 of their papers we have counts for
10 papers
Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows
Jiayi Cai, Pierre-Emmanuel Angeli, Jean-Marc Martinez +2
Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence RANS modeling have recently been proposed in the literature as data-driven constitutive models f…
Deep Learning reconstruction with uncertainty estimation for photon interaction in fast scintillator detectors
Geoffrey Daniel, Mohamed Bahi Yahiaoui, Claude Comtat +7
This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, wit…
Packed-Ensembles for Efficient Uncertainty Estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione +4
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detec…
Gaussian linear approximation for the estimation of the Shapley effects
Baptiste Broto, François Bachoc, Marine Depecker +1
In this paper, we address the estimation of the sensitivity indices called "Shapley eects". These sensitivity indices enable to handle dependent input variables. The Shapley eects…
Block-diagonal covariance estimation and application to the Shapley effects in sensitivity analysis
Baptiste Broto, François Bachoc, Laura Clouvel +1
In this paper, we aim to estimate block-diagonal covariance matrices for Gaussian data in high dimension and in fixed dimension. We first estimate the block-diagonal structure of t…
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines
Rémi Sainct, Cyril Feau, Jean-Marc Martinez +1
Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Pr…
The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis
J-B. Blanchard, G. Damblin, J-M. Martinez +2
The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted, bring…
Sensitivity indices for independent groups of variables
Baptiste Broto, François Bachoc, Marine Depecker +1
In this paper, we study sensitivity indices for independent groups of variables and we look at the particular case of block-additive models. We show in this case that most of the S…
Improvement of code behaviour in a design of experiments by metamodeling
François Bachoc, Jean-Marc Martinez, Karim Ammar
It is now common practice in nuclear engineering to base extensive studies on numerical computer models. These studies require to run computer codes in potentially thousands of num…
Calibration and improved prediction of computer models by universal Kriging
François Bachoc, Guillaume Bois, Josselin Garnier +1
This paper addresses the use of experimental data for calibrating a computer model and improving its predictions of the underlying physical system. A global statistical approach is…