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dc.contributor.authorWu, Dawen
dc.contributor.authorChamoin, Ludovic
dc.date.accessioned2026-08-20T21:18:00Z
dc.date.available2026-08-20T21:18:00Z
dc.date.copyright2025/10/29
dc.date.issued2026
dc.identifier.otherAJRUA6.RUENG-1702.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314253
dc.description.abstractAbstractThis study addresses the challenge of estimating Bayesian posterior distributions, a key task in uncertainty quantification, particularly within settings where the mapping from parameters to observations is a black box. Such models, often ...Practical ApplicationsThis research presents a computational method to better understand complex systems (like heat flow or population dynamics) when we can only run simulations forward. This is often the case with complicated simulation software or ...
dc.publisherAmerican Society of Civil Engineers
dc.titleTwo-Stage Framework Combining Derivative-Free MCMC Sampling and Transport Maps for Black-Box Bayesian Inverse Problems
dc.typeJournal Article
dc.journal.volume12
dc.journal.issue1
dc.journal.titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi10.1061/AJRUA6.RUENG-1702
dc.journal.fristpage04025094-1
dc.journal.lastpage04025094-12
dc.page12
dc.treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttypeFulltext


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