34 citations · 75 across the 8 of their papers we have counts for
8 papers
Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition
Han Wang, Eiji Kawasaki, Guillaume Damblin +1
We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Ba…
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…
A Gaussian process and linear-based framework for computing cut distributions in modular Bayesian calibration of two chained computer models
Oumar Baldé, Guillaume Damblin, Amandine Marrel +2
Computer models are widely used in science and engineering to simulate complex systems. However, these models are affected by several sources of uncertainty, which may limit their…
A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments
Guillaume Damblin, François Bachoc, Sandro Gazzo +2
The semi-empirical nature of best-estimate models closing the balance equations of thermal-hydraulic (TH) system codes is well-known as a significant source of uncertainty for accu…
Reynolds Stress Anisotropy Tensor Predictions for Turbulent Channel Flow using Neural Networks
Jiayi Cai, Pierre-Emmanuel Angeli, Jean-Marc Martinez +2
The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reyno…
Adaptive use of replicated Latin Hypercube Designs for computing Sobol' sensitivity indices
Guillaume Damblin, Alberto Ghione
As recently pointed out in the field of Global Sensitivity Analysis (GSA) of computer simulations, the use of replicated Latin Hypercube Designs (rLHDs) is a cost-saving alternativ…
Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes
Guillaume Damblin, Pierre Gaillard
Uncertainty Quantification of closure relationships integrated into thermal-hydraulic system codes is a critical prerequisite in applying the Best-Estimate Plus Uncertainty (BEPU)…
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…