9 papers
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…
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 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…
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
Giovanni Ziarelli, Stefano Pagani, Nicola Parolini +2
In this work, we aim to formalize a novel scientific machine learning framework to reconstruct the hidden dynamics of the transmission rate, whose inaccurate extrapolation can sign…
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…