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, and used...

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Detalles Bibliográficos
Autores principales: Ranftl, Sascha, Melito, Gian Marco, Badeli, Vahid, Reinbacher-Köstinger, Alice, Ellermann, Katrin, von der Linden, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516489/
https://www.ncbi.nlm.nih.gov/pubmed/33285833
http://dx.doi.org/10.3390/e22010058

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516489/
https://www.ncbi.nlm.nih.gov/pubmed/33285833
http://dx.doi.org/10.3390/e22010058

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