From the 1 of 7 linked papers with an AI index.
7 papers
Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics
Irena RadiÅ¡iÄ, Raffaele Tirotta, Alberto Zingaro +2
The paper introduces a physics-informed neural network (PINN) framework that combines incompressible Navier‑Stokes equations with sparse experimental velocity data to reconstruct h…
Physics-constrained identification of graph-based thermal networks for spacecraft digital twins
Luca Sosta, Carlo Ciancarelli, Leonardo Marini +3
Reconstructing a thermal model capable of efficiently simulating the behavior of a spacecraft from sparse and localized temperature measurements remains a challenging task. To addr…
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
Davide Carrara, Marc Hirschvogel, Francesca Bonizzoni +3
High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate…
Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs
Riccardo Tenderini, Luca Pegolotti, Fanwei Kong +4
This work introduces AD-SVFD, a deep learning model for the deformable registration of vascular shapes to a pre-defined reference and for the generation of synthetic anatomies. AD-…
Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models
Matthias Höfler, Francesco Regazzoni, Stefano Pagani +5
Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical…
Influence of cellular mechano-calcium feedback in numerical models of cardiac electromechanics
Irena RadiÅ¡iÄ, Francesco Regazzoni, Michele Bucelli +3
Multiphysics and multiscale mathematical models enable the non-invasive study of cardiac function. These models often rely on simplifying assumptions that neglect certain biophysic…