G. Damblin

发表

Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties

Bertrand Iooss, G. Damblin, M. Couplet, 2013, J. Simulation.

Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing

F. Ruggeri, M. Keller, P. Barbillon, 2018, Econometrics.

Extension of the CIRCE methodology to improve the Inverse Uncertainty Quantification of several combined thermal-hydraulic models

D. Lucor, G. Damblin, Alberto Ghione, 2022, Nuclear Engineering and Design.

Reynolds Stress Anisotropy Tensor Predictions for Turbulent Channel Flow using Neural Networks

D. Lucor, G. Damblin, Jean-Marc Martinez, 2022, 2208.14301.

SAPIUM: A Generic Framework for a Practical and Transparent Quantification of Thermal-Hydraulic Code Model Input Uncertainty

Jinzhao Zhang, Francesc Reventos, Rafael Mendizábal, 2020 .

Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes

G. Damblin, P. Gaillard, 2019, Nuclear Engineering and Design.

Adaptive Numerical Designs for the Calibration of Computer Codes

Merlin Keller, Pierre Barbillon, Eric Parent, 2015, SIAM/ASA J. Uncertain. Quantification.

Bayesian Model Selection for the Validation of Computer Codes

Merlin Keller, Pierre Barbillon, Alberto Pasanisi, 2016, Qual. Reliab. Eng. Int..

Development of good practice guidance for quantification of thermal-hydraulic code model input uncertainty

Jinzhao Zhang, Francesc Reventos, Rafael Mendizábal, 2019 .

The Uranie platform: an open-source software for optimisation, meta-modelling and uncertainty analysis

G. Arnaud, J-B. Blanchard, G. Damblin, 2018, EPJ Nuclear Sciences & Technologies.

SAPIUM a systematic approach for input uncertainty quantification

Francesc Reventos, Jean Baccou, Fabrice Fouet, 2018 .

A comprehensive Bayesian framework for the development, validation and uncertainty quantification of thermal-hydraulic models

D. Lucor, G. Damblin, Alberto Ghione, 2022, Annals of Nuclear Energy.

Nonparametric Bayesian approach for quantifying the conditional uncertainty of input parameters in chained numerical models

A. Marrel, G. Damblin, L. Giraldi, 2023, 2307.01111.

VALIDATION OF COMPUTER MODELS

Merlin Keller, Pierre Barbillon, Éric Parent, 2013 .

Adaptive use of replicated Latin Hypercube Designs for computing Sobol' sensitivity indices

Alberto Ghione, Guillaume Damblin, G. Damblin, 2021, Reliab. Eng. Syst. Saf..