activity
20202026
most citedDeep learning-based reduced order models in cardiac electrophysiology

77 citations · 197 across the 20 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.LG2022★ 77 cited

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Paolo Conti, Giorgio Gobat, Stefania Fresca +2

Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, whi…

math.NA2022

Approximation bounds for convolutional neural networks in operator learning

Nicola Rares Franco, Stefania Fresca, Andrea Manzoni +1

Recently, deep Convolutional Neural Networks (CNNs) have proven to be successful when employed in areas such as reduced order modeling of parametrized PDEs. Despite their accuracy…

math.DS2022★ 13 cited

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

Giorgio Gobat, Stefania Fresca, Andrea Manzoni +1

Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless app…

math.NA2022★ 1 cited

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation

Ludovica Cicci, Stefania Fresca, Andrea Manzoni +1

Reducing the computational time required by high-fidelity, full order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient…

math.NA2022★ 1 cited

Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs

Ludovica Cicci, Stefania Fresca, Andrea Manzoni

To speed-up the solution to parametrized differential problems, reduced order models (ROMs) have been developed over the years, including projection-based ROMs such as the reduced-…

math.NA2022★ 4 cited

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Federico Fatone, Stefania Fresca, Andrea Manzoni

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e.g., exclusively through proper…