Bayesian Methods For Uncertainty Quantification
Loading...
Files
No Access Until
Permanent Link(s)
Collections
Other Titles
Author(s)
Abstract
Computer codes simulating physical systems usually have responses that consist of a set of distinct outputs (e.g., velocity and pressure) that evolve also in space and time and depend on many unknown input parameters (e.g., physical constants, initial/boundary conditions, etc.). Furthermore, essential engineering procedures such as uncertainty quantification, inverse problems or design are notoriously difficult to carry out mostly due to the limited simulations available. The aim of this work is to introduce a fully Bayesian approach for treating these problems which accounts for the uncertainty induced by the infinite number of observations.
Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
2013-05-26
Publisher
Keywords
Bayesian; uncertainty quantification; computer surrogate; probability; limited simulations; expensive solvers
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Zabaras, Nicholas John
Committee Co-Chair
Committee Member
Bindel, David S.
Vladimirsky, Alexander B.
Samorodnitsky, Gennady
Vladimirsky, Alexander B.
Samorodnitsky, Gennady
Degree Discipline
Applied Mathematics
Degree Name
Ph. D., Applied Mathematics
Degree Level
Doctor of Philosophy
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Rights URI
Types
dissertation or thesis