Stefania Fresca

According to our database, Stefania Fresca authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition.

Simone Brivio, Stefania Fresca, Nicola Rares Franco, Andrea Manzoni

Adv. Comput. Math., June, 2024

On latent dynamics learning in nonlinear reduced order modeling.

Nicola Farenga, Stefania Fresca, Simone Brivio, Andrea Manzoni

CoRR, 2024

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs.

Simone Brivio, Stefania Fresca, Andrea Manzoni

CoRR, 2024

2023

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression.

Ludovica Cicci, Stefania Fresca, Mengwu Guo, Andrea Manzoni, Paolo Zunino

Comput. Math. Appl., November, 2023

Approximation bounds for convolutional neural networks in operator learning.

Nicola Rares Franco, Stefania Fresca, Andrea Manzoni, Paolo Zunino

Neural Networks, April, 2023

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches.

Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi

Sensors, March, 2023

Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks.

Nicola Rares Franco, Stefania Fresca, Filippo Tombari, Andrea Manzoni

CoRR, 2023

2022

Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs.

Ludovica Cicci, Stefania Fresca, Andrea Manzoni

J. Sci. Comput., 2022

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

Paolo Conti, Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi

CoRR, 2022

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

Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi

CoRR, 2022

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

Ludovica Cicci, Stefania Fresca, Andrea Manzoni, Alfio Quarteroni

CoRR, 2022

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

Federico Fatone, Stefania Fresca, Andrea Manzoni

CoRR, 2022

2021

A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs.

Stefania Fresca, Luca Dedè, Andrea Manzoni

J. Sci. Comput., 2021

Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures.

Stefania Fresca, Giorgio Gobat, Patrick Fedeli, Attilio Frangi, Andrea Manzoni

CoRR, 2021

Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition.

Giorgio Gobat, Andrea Opreni, Stefania Fresca, Andrea Manzoni, Attilio Frangi

CoRR, 2021

Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models.

Stefania Fresca, Andrea Manzoni

CoRR, 2021

POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition.

Stefania Fresca, Andrea Manzoni

CoRR, 2021

2020

Deep learning-based reduced order models in cardiac electrophysiology.

Stefania Fresca, Andrea Manzoni, Luca Dedè, Alfio Quarteroni

CoRR, 2020