Stefania Fresca

44/2025

Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROM

BRIVIO, S.; FRESCA, S.; MANZONI, A. July 16, 2025

31/2025

HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs

BOTTEGHI, N.; FRESCA, S.; GUO, M.; MANZONI, A. May 22, 2025

100/2024

On latent dynamics learning in nonlinear reduced order modeling

FARENGA, N.; FRESCA, S.; BRIVIO, S.; MANZONI, A. November 27, 2024

94/2024

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

FRANCO, N.R.; FRESCA, S.; TOMBARI, F.; MANZONI, A. November 11, 2024

96/2024

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

BRIVIO, S.; FRESCA, S.; MANZONI, A. November 11, 2024

85/2024

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

BRIVIO, S.; FRANCO, NICOLA R.; FRESCA, S.; MANZONI, A. November 9, 2024

05/2024

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

CONTI, P.; GOBAT, G.; FRESCA, S.; MANZONI, A.; FRANGI, A. January 24, 2024

105/2023

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

CICCI, L.; FRESCA, S.; GUO, M.; MANZONI, A.; ZUNINO, P. December 16, 2023

66/2023

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

FRESCA, S.; GOBAT, G.; FEDELI, P.; FRANGI, A.; MANZONI, A. September 5, 2023

MOX

45/2022

Approximation bounds for convolutional neural networks in operator learning

FRANCO, N.; FRESCA, S.; MANZONI, A.; ZUNINO, P. July 13, 2022

MOX

10/2022

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

FRESCA, S.; MANZONI, A. February 22, 2022

MOX

08/2022

Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition

GOBAT, G.; OPRENI, A.; FRESCA, S.; MANZONI, A.; FRANGI, A. February 2, 2022

MOX

75/2021

Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics

CICCI, L.; FRESCA, S.; PAGANI, S.; MANZONI, A.; QUARTERONI, A. November 28, 2021

MOX

72/2021

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

FRESCA, S.; MANZONI, A. November 16, 2021

MOX

40/2020

Deep learning-based reduced order models in cardiac electrophysiology

FRESCA, S.; MANZONI, A.; DEDè, L.; QUARTERONI, A. June 7, 2020

MOX

02/2020

A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

FRESCA, S.; DEDE', L.; MANZONI, A. January 10, 2020