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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/34013
Title: Bayesian Methods For Uncertainty Quantification
Authors: Bilionis, Ilias
Keywords: Bayesian
uncertainty quantification
computer surrogate
probability
limited simulations
expensive solvers
Issue Date: 26-May-2013
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.
Committee Chair: Zabaras, Nicholas John
Committee Member: Bindel, David S.
Vladimirsky, Alexander B.
Samorodnitsky, Gennady
Discipline: Applied Mathematics
Degree Name: Ph.D. of Applied Mathematics
Degree Level: Doctor of Philosophy
Degree Grantor: Cornell University
No Access Until: 2018-05-27
URI: http://hdl.handle.net/1813/34013
Appears in Collections:Theses and Dissertations (CLOSED)

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