5 papers
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…
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…
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…
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…
Optimal patient allocation for echocardiographic assessments
Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein +3
Scheduling echocardiographic exams in a hospital presents significant challenges due to non-deterministic factors (e.g., patient no-shows, patient arrival times, diverse exam durat…