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Title:

Data-Consistent Solutions to Stochastic Inverse Problems Using a Probabilistic Multi-Fidelity Method Based on Conditional Densities

Document type:
Zeitschriftenaufsatz
Author(s):
Bruder, L., Gee, M. W., Wildey, T.
Abstract:
We build upon a recently developed approach for solving stochastic inverse problems based on a combination of measure-theoretic principles and Bayes' rule. We propose a multi-fidelity method to reduce the computational burden of performing uncertainty quantification using high-fidelity models. This approach is based on a Monte Carlo framework for uncertainty quantification that combines information from solvers of various fidelities to obtain statistics on the quantities of interest of the probl...     »
Keywords:
multi-fidelity; stochastic inverse problems; uncertainty quantification; stochastic regression
Journal title:
International Journal for Uncertainty Quantification
Year:
2020
Journal volume:
10
Journal issue:
5
Covered by:
Web of Science
Fulltext / DOI:
doi:10.1615/Int.J.UncertaintyQuantification.2020030092
Status:
Verlagsversion / published
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