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...
Main Authors: | Sascha Ranftl, Gian Marco Melito, Vahid Badeli, Alice Reinbacher-Köstinger, Katrin Ellermann, Wolfgang von der Linden |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-12-01
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Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/22/1/58 |
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