59 citations · 217 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Vahidullah Tac, Manuel K Rausch, Ilias Bilionis +2
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy fu…
Physics-informed neural networks for blood flow inverse problems
Jeremias Garay, Jocelyn Dunstan, Sergio Uribe +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known a…
Shape of my heart: Cardiac models through learned signed distance functions
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +6
The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear st…
Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields
Tabita Catalán, Matías Courdurier, Axel Osses +3
Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelera…
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal +1
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy fun…