2

Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
We propose a non-intrusive deep learning-based reduced order model(DL-ROM) capable of capturing the complex dynamics of mechanical …
Stefania Fresca, Giorgio Gobat, Patrick Fedeli, Attilio Frangi, Andrea Manzoni
PDF DOI
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
We apply the Proper Orthogonal Decomposition (POD) method for the efficient simulation of several scenarios undergone by …
Giorgio Gobat, Andrea Opreni, Stefania Fresca, Andrea Manzoni, Attilio Frangi
PDF DOI
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics
The numerical simulation of several virtual scenarios arising in cardiac mechanics poses a computational challenge that can be …
Ludovica Cicci, Stefania Fresca, Stefano Pagani, Andrea Manzoni, Alfio Quarteroni
PDF DOI
Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics
POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional …
Stefania Fresca, Andrea Manzoni
PDF Code DOI
POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
POD-enhanced deep learning-based reduced order models for the real-time simulation of cardiac electrophysiology in the left atrium
The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems …
Stefania Fresca, Andrea Manzoni, Luca Dede', Alfio Quarteroni
PDF DOI
POD-enhanced deep learning-based reduced order models for the real-time simulation of cardiac electrophysiology in the left atrium
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, …
Stefania Fresca, Andrea Manzoni
PDF Code DOI
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Conventional reduced order modeling techniques such as the reduced basis (RB) method (relying, e.g., on proper orthogonal decomposition …
Stefania Fresca, Andrea Manzoni, Luca Dede'
PDF Code DOI
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Deep learning-based reduced order models in cardiac electrophysiology
Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of …
Stefania Fresca, Andrea Manzoni, Luca Dede', Alfio Quarteroni
PDF Code DOI
Deep learning-based reduced order models in cardiac electrophysiology