2 citations · 3 across the 3 of their papers we have counts for
4 papers
Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification
Karthik Menon, Andrea Zanoni, Owais Khan +4
Simulations of coronary hemodynamics have improved non-invasive clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical i…
Bayesian Windkessel calibration using optimized 0D surrogate models
Jakob Richter, Jonas Nitzler, Luca Pegolotti +6
Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibrati…
Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques
Andrea Zanoni, Gianluca Geraci, Matteo Salvador +3
We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-f…
A Probabilistic Neural Twin for Treatment Planning in Peripheral Pulmonary Artery Stenosis
John D. Lee, Jakob Richter, Martin R. Pfaller +8
The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in dat…