3 papers
stat.ML2026
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
Teng Li, Stephen Wu, Yong Huang +2
The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from meas…
stat.AP2026
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
Xianghao Meng, James L. Beck, Yong Huang +1
In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monito…
stat.AP2026
MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
Xianghao Meng, Yong Huang, James L. Beck +2
Over decades, Markov chain Monte Carlo (MCMC) methods have been widely studied, with a typical application being the quantification of posterior uncertainties in Bayesian system id…