Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations. They assumed each level to be describable by a Gaussian process...

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Bibliographic Details
Main Authors: Sascha Ranftl, Gian Marco Melito, Vahid Badeli, Alice Reinbacher-Köstinger, Katrin Ellermann, Wolfgang von der Linden
Format: Article
Language:English
Published: MDPI AG 2019-12-01
Series:Entropy
Subjects:
uncertainty quantification
multi fidelity
gaussian processes
probability theory
bayes
impedance cardiography
aortic dissection
Online Access:https://www.mdpi.com/1099-4300/22/1/58

Internet

https://www.mdpi.com/1099-4300/22/1/58

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