4 papers
Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates
Natalia L. Rubio, Eric F. Darve, Alison L. Marsden
Physics-based 0D reduced-order models provide computationally lightweight predictions of cardiovascular flows, resolving bulk hemodynamics in fractions of a second that would take…
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi
Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational…
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…
Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein +3
Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variable arrival times, a…