3 papers
cs.LG2025
Assessing the performance of correlation-based multi-fidelity neural emulators
Cristian J. Villatoro, Gianluca Geraci, Daniele E. Schiavazzi
Outer loop tasks such as optimization, uncertainty quantification or inference can easily become intractable when the underlying high-fidelity model is computationally expensive. S…
math.NA2025
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…
physics.flu-dyn2024
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…