Benutzer: Gast  Login
Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
multi-fidelity; stochastic inverse problems; uncertainty quantification; stochastic regression
Zeitschriftentitel:
International Journal for Uncertainty Quantification
Jahr:
2020
Band / Volume:
10
Heft / Issue:
5
Nachgewiesen in:
Web of Science
Volltext / DOI:
doi:10.1615/Int.J.UncertaintyQuantification.2020030092
Status:
Verlagsversion / published
 BibTeX