collaborators

5 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.NA2026

Learned Finite Element-based Regularization of the Inverse Problem in Electrocardiographic Imaging

Manuel Haas, Thomas Grandits, Thomas Pinetz +3

Electrocardiographic imaging (ECGI) seeks to reconstruct cardiac electrical activity from body-surface potentials noninvasively. However, the associated inverse problem is severely…

math.NA2025

Finite element-based space-time total variation-type regularization of the inverse problem in electrocardiographic imaging

Manuel Haas, Thomas Grandits, Thomas Pinetz +3

Reconstructing cardiac electrical activity from body surface electric potential measurements results in the severely ill-posed inverse problem in electrocardiography. Many differen…

math.OC2025

Optimal dynamic thermal plant control: A study and benchmark

Thomas Grandits, Stefano Coss, Gundolf Haase

District heating networks play a vital role in thermal energy supply in many countries. Thus, it comes to no surprise that these has been a central role in improving energy efficie…

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…