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dc.contributor.authorBi, Sifeng
dc.contributor.authorBeer, Michael
dc.contributor.authorOuisse, Morvan
dc.contributor.authorCogan, Scott
dc.date.accessioned2022-02-04T14:29:06Z
dc.date.available2022-02-04T14:29:06Z
dc.date.copyright2020/05/14/
dc.date.issued2020
dc.identifier.issn2332-9017
dc.identifier.otherrisk_006_03_030301.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4273758
dc.description.abstractIt has been widely recognized that the inevitable uncertainties in both operational experiments and numerical analyses require efforts to be addressed appropriately within the overall area of Computational Mechanics Engineering. This endeavor includes critical tasks such as numerical model calibration, updating, verification, and validation. Nondeterministic modeling approaches enable characterization, propagation, and quantification of the inevitable uncertainties, providing model predictions over a possible range of outcomes (distributional, interval, fuzzy, etc.) rather than a unique solution with maximum fidelity to a single experiment. Such approaches applied in structural dynamics significantly promote the tendency toward high accuracy and robustness in computer-aided engineering.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleSpecial Section on Uncertainty Management in Complex Multiphysics Structural Dynamics
dc.typeJournal Paper
dc.journal.volume6
dc.journal.issue3
dc.journal.titleASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
dc.identifier.doi10.1115/1.4047097
dc.page30301
dc.treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2020:;volume( 006 ):;issue: 003
dc.contenttypeFulltext


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