3 citations · 6 across the 6 of their papers we have counts for
6 papers
A Neural Latent Dynamics Approach for Solving Inverse Problems in Cardiac Electrophysiology
Edoardo Centofanti, Giovanni Ziarelli, Simone Scacchi +1
Solving inverse problems in cardiac electrophysiology consists in the recovery of physiological parameters from surface electrocardiogram (ECG) measurements, a task which is often…
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
Luca Pellegrini, Massimiliano Ghiotto, Edoardo Centofanti +1
Ionic models, described by systems of stiff ordinary differential equations, are fundamental tools for simulating the complex dynamics of excitable cells in both Computational Neur…
Learning cardiac activation and repolarization times with operator learning
Edoardo Centofanti, Giovanni Ziarelli, Nicola Parolini +3
Solving partial or ordinary differential equation models in cardiac electrophysiology is a computationally demanding task, particularly when high-resolution meshes are required to…
Learning the Hodgkin-Huxley Model with Operator Learning Techniques
Edoardo Centofanti, Massimiliano Ghiotto, Luca F. Pavarino
We construct and compare three operator learning architectures, DeepONet, Fourier Neural Operator, and Wavelet Neural Operator, in order to learn the operator mapping a time-depend…
A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU-GPU architectures
Edoardo Centofanti, Simone Scacchi
The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardia…
A massless interacting Fermionic Cellular Automaton exhibiting bound states
Edoardo Centofanti, Alessandro Bisio, Paolo Perinotti
We present a Fermionic Cellular Automaton model which describes massless Dirac fermion in 1+1 dimension coupled with local, number preserving interaction. The diagonalization of th…