most citedLatent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes

5 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2023

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Pasquale C. Africa, Roberto Piersanti, Francesco Regazzoni +6

Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-…

math.NA20233 cited

Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Matteo Salvador, Marina Strocchi, Francesco Regazzoni +3

Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain…

math.NA2023

Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: application to cardiac electromechanics

Michele Bucelli, Francesco Regazzoni, Luca Dede' +1

The accurate robust and efficient transfer of the deformation gradient tensor between meshes of different resolution is crucial in cardiac electromechanics simulations. We present…

cs.LG20235 cited

Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes

Francesco Regazzoni, Stefano Pagani, Matteo Salvador +2

Predicting the evolution of systems that exhibit spatio-temporal dynamics in response to external stimuli is a key enabling technology fostering scientific innovation. Traditional…

cs.CE2023

A comprehensive mathematical model for cardiac perfusion

Alberto Zingaro, Christian Vergara, Luca Dede' +2

We present a novel mathematical model that simulates myocardial blood perfusion by embedding multiscale and multiphysics features. Our model incorporates cardiac electrophysiology,…