Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data

DOI10.1016/j.cma.2023.116721arXiv2307.05592OpenAlexW4390628658MaRDI QIDQ6194191

Mahmoud Yaseen, Xu Wu, Ziyu Xie

Publication date: 19 March 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2307.05592

zbMATH Keywords

neural networksfunctional PCAfunctional alignmentBayesian inverse UQ

Mathematics Subject Classification ID

Statistics (62-XX) Biology and other natural sciences (92-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Generative models for functional data using phase and amplitude separation
• Alignment of curves by dynamic time warping
• The design and analysis of computer experiments.
• X-TMCMC: adaptive kriging for Bayesian inverse modeling
• Bayesian neural networks: an introduction and survey
• Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification
• Parameter estimation with maximal updated densities
• Bayesian Calibration of Computer Models
• Computer Model Calibration Using High-Dimensional Output
• Curve Registration
• Combining Field Data and Computer Simulations for Calibration and Prediction
• Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation–maximization under Bayesian framework
• Stochastic modeling and statistical calibration with model error and scarce data

This page was built for publication: Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data