3 papers
eess.IV2026
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +3
Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point clouds. However, these methods are…
math.OC2025
Accurate and Efficient Cardiac Digital Twin from surface ECGs: Insights into Identifiability of Ventricular Conduction System
Thomas Grandits, Karli Gillette, Gernot Plank +1
Digital twins for cardiac electrophysiology are an enabling technology for precision cardiology. Current forward models are advanced enough to simulate the cardiac electric activit…
cs.LG2025
-PINNs: physics-informed neural networks on complex geometries
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expa…