activity
20222026
most cited-PINNs: physics-informed neural networks on complex geometries

58 citations · 60 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CG2026

iLogMap: Geodesic Polar Coordinates Parameterization with the Magnetic Laplacian

Tomás Banduc, Simone Pezzuto, Francisco Sahli Costabal

Geodesic polar coordinates (GPCs) provide an intrinsic parameterization over curved surfaces, but their accurate estimation remains challenging, particularly in the presence of ani…

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.OC2024

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.LG2024★ 1 cited

Ensemble learning of the atrial fiber orientation with physics-informed neural networks

Efraín Magaña, Simone Pezzuto, Francisco Sahli Costabal

The anisotropic structure of the myocardium is a key determinant of the cardiac function. To date, there is no imaging modality to assess in-vivo the cardiac fiber structure. We re…

stat.ML2023★ 1 cited

Probabilistic learning of the Purkinje network from the electrocardiogram

Felipe Álvarez-Barrientos, Mariana Salinas-Camus, Simone Pezzuto +1

The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. H…

cs.LG2022★ 58 cited

-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…