Kevin de Vries, alumnus of the Computational Science Lab, together with his daily supervisors Anna Nikishova and Ben Czaja, and scientific advisors Gabor Zavodszky and Alfons Hoekstra published his master thesis work on “Inverse Uncertainty Quantification of a cell model using a Gaussian Process metamodel” in the International Journal for Uncertainty Quantification. The goal of this study was to identify uncertainty in the inputs of the material model for Red Blood Cells from the HemoCell library. The model is computationally expensive and, in order to obtain a large enough sample size for reliable uncertainty estimation, a Gaussian Process regression metamodel was trained. Additionally, the identifiability of the model parameters was estimated using Sobol sensitivity indices. The results and conclusions of the study can be found in the article following the link: https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020033186.
- July 24, 2020Inverse Uncertainty Quantification of a cell model using a Gaussian Process metamodel
- April 17, 2020Virtual flow diverter implantation with realistic deployment mechanics and validated force response
- March 20, 2020Quantifying systemic risk using Bayesian networks
- March 18, 2020The influence of red blood cell deformability on hematocrit profiles and platelet margination
- March 11, 2020Two interdisciplinary Postdoc (3y) positions with DIEP@UvA