[ 1 ] Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data,International Journal of Heat and Mass Transfer,2025,
[ 2 ] A systematic approach for the adequacy analysis of a set of experimental databases: Application in the framework of the ATRIUM activity,Nuclear Engineering and Design,2024,
[ 3 ] Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data,Computer Methods in Applied Mechanics and Engineering,2024,
[ 4 ] Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification,Annals of Nuclear Energy,2023,
[ 5 ] Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data,Annals of Nuclear Energy,2022,
[ 6 ] A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes,Nuclear Engineering and Design,2021,
