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