3 citations · 4 across the 4 of their papers we have counts for
6 papers
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi +1
Solving inverse problems in cardiovascular modeling is particularly challenging due to the high computational cost of running high-fidelity simulations. In this work, we focus on B…
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
Gianluca Geraci, Daniele E. Schiavazzi, Andrea Zanoni
We consider the problem of propagating the uncertainty from a possibly large number of random inputs through a computationally expensive model. Stratified sampling is a well-known…
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…
Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
Andrea Zanoni, Gianluca Geraci, Matteo Salvador +2
We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a on…
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…