Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood Cell-Reference-Cited by-同舟云学术

Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood Cell

Published:2021-03-22 Issue:3 Volume:15 Page:
ISSN:2331-7019
Container-title:Physical Review Applied
language:en
Short-container-title:Phys. Rev. Applied

Author:

Economides Athena,Arampatzis Georgios,Alexeev Dmitry^ORCID,Litvinov Sergey,Amoudruz Lucas^ORCID,Kulakova Lina,Papadimitriou Costas^ORCID,Koumoutsakos Petros^ORCID

Funder

European Research Council

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevApplied.15.034062/fulltext

Reference92 articles.

1. Numerical Simulation of Flowing Blood Cells

2. Red Blood Cell Shapes and Shape Transformations: Newtonian Mechanics of a Composite Membrane: Sections 2.1–2.4

3. Dynamics of a large population of red blood cells under shear flow

4. Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness

5. Classification of red blood cell shapes in flow using outlier tolerant machine learning

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