Stefania Fresca

Assistant Professor

Contact Information

Phone:
+39 02 2399
Fax:
+39 02 2399
Office:
14 (Nave)
Email:
Personal web page:

Keywords

Computational learning
Advanced Numerical Methods for Scientific Computing
Computational Medicine for the Cardiocirculatory System

Publications

   

Available MOX Reports

CONTI, P.; GOBAT, G.; FRESCA, S.; MANZONI, A.; FRANGI, A.
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
CICCI, L.; FRESCA, S.; GUO, M.; MANZONI, A.; ZUNINO, P.
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
FRESCA, S.; GOBAT, G.; FEDELI, P.; FRANGI, A.; MANZONI, A.
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
FRANCO, N.; FRESCA, S.; MANZONI, A.; ZUNINO, P.
Approximation bounds for convolutional neural networks in operator learning
FRESCA, S.; MANZONI, A.
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
GOBAT, G.; OPRENI, A.; FRESCA, S.; MANZONI, A.; FRANGI, A.
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
CICCI, L.; FRESCA, S.; PAGANI, S.; MANZONI, A.; QUARTERONI, A.
Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics
FRESCA, S.; MANZONI, A.
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
FRESCA, S.; MANZONI, A.; DEDè, L.; QUARTERONI, A.
Deep learning-based reduced order models in cardiac electrophysiology
FRESCA, S.; DEDE', L.; MANZONI, A.
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs