Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data

10 July 2023

Papers citing "Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data"

7 / 7 papers shown

Title
Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks L. Moloko P. Bokov Xu Wu K. Ivanov 18 9 0 16 Nov 2022
Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models M. Yaseen Xu Wu AI4CE 53 14 0 27 Jun 2022
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 62 205 0 22 Jun 2020
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 P. Gaillard 45 20 0 13 Feb 2019
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory Xu Wu T. Kozłowski Hadi Meidani K. Shirvan 51 100 0 05 Jan 2018
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 282 4,793 0 04 Jan 2016
Generative Models for Functional Data using Phase and Amplitude Separation J. D. Tucker Wei Wu Anuj Srivastava 68 191 0 08 Dec 2012