Nicola Rares Franco

Post-doctoral Research Fellow

Contact Information

Phone:
+39 02 2399 4564
Fax:
+39 02 2399
Office:
Ed. 14 (Nave)
Email:

Keywords

Computational learning
Advanced Numerical Methods for Scientific Computing
Statistical learning
Health Analytics
Living Systems and Precision Medicine

Publications

    

Available MOX Reports

FRANCO, N.R.; FRESCA, S.; TOMBARI, F.; MANZONI, A.
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks
BRIVIO, S.; FRANCO, NICOLA R.; FRESCA, S.; MANZONI, A.
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition
FRANCO, N.R.; FRAULIN, D.; MANZONI, A.; ZUNINO, P.
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
VITULLO, P.; FRANCO, N.R.; ZUNINO, P.
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
FRANCO, N.R.; BRUGIAPAGLIA, S.
A practical existence theorem for reduced order models based on convolutional autoencoders
VITULLO, P.; COLOMBO, A.; FRANCO, N.R.; MANZONI, A.; ZUNINO, P.
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
FRANCO, N.R; MANZONI, A.; ZUNINO, P.
Learning Operators with Mesh-Informed Neural Networks
FRANCO, N.; FRESCA, S.; MANZONI, A.; ZUNINO, P.
Approximation bounds for convolutional neural networks in operator learning
FRANCO, N.; MANZONI, A.; ZUNINO, P.
A Deep Learning approach to Reduced Order Modelling of parameter dependent Partial Differential Equations
MASSI, M.C.; FRANCO, N.R; IEVA, F.; MANZONI, A.; PAGANONI, A.M.; ZUNINO, P.
High-Order Interaction Learning via Targeted Pattern Search
MASSI, M.C., GASPERONI, F., IEVA, F., PAGANONI, A.M., ZUNINO, P., MANZONI, A., FRANCO, N.R., ET AL.
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort