4 papers · 1 filter
Learning PDE solution operators with variable initial conditions via Latent Dynamics Networks
Stefano Maria Pizzamiglio, Stefano Pagani, Francesco Regazzoni
In many-query scenarios, data-driven surrogate models provide an efficient alternative to high-fidelity solvers for simulating physical systems governed by Partial Differential Equ…
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…
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…