Fast and Scalable Likelihood-Free Bayesian Model Updating for Multiple-Measurement Data via Conditional Invertible Neural Network and Posterior Stacking

Abstract

AbstractBayesian model updating (BMU) is widely used in structural health monitoring (SHM) to calibrate model parameters and quantify uncertainties. However, a key challenge in BMU is the intractability of the likelihood function, which leads to high ...