4 papers · 1 filter
Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics
Irena Radišić, Raffaele Tirotta, Alberto Zingaro +2
Accurate, spatially resolved flow field measurements are essential for the reliable assessment of hemodynamic quantities in cardiovascular research and clinical practice. Experimen…
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…
Influence of cellular mechano-calcium feedback in numerical models of cardiac electromechanics
Irena Radišić, 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…
Shape-informed surrogate models based on signed distance function domain encoding
Linying Zhang, Stefano Pagani, Jun Zhang +1
We propose a non-intrusive method to build surrogate models that approximate the solution of parameterized partial differential equations (PDEs), capable of taking into account the…