From the 1 of 7 linked papers with an AI index.
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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…
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…
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…