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

dc.contributor.author	Zeng, Jice
dc.contributor.author	Xue, Kaiyi
dc.contributor.author	Chen, Hui
dc.date.accessioned	2026-08-20T21:17:33Z
dc.date.available	2026-08-20T21:17:33Z
dc.date.copyright	2025/10/30
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1692.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314242
dc.description.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 ...
dc.publisher	American Society of Civil Engineers
dc.title	Fast and Scalable Likelihood-Free Bayesian Model Updating for Multiple-Measurement Data via Conditional Invertible Neural Network and Posterior Stacking
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	1
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1692
dc.journal.fristpage	04025096-1
dc.journal.lastpage	04025096-18
dc.page	18
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttype	Fulltext