96/2024 - Brivio, S.; Fresca, S.; Manzoni, A.
PTPI-DL-ROMs: Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
95/2024 - Zacchei, F.; Rizzini, F.; Gattere, G.; Frangi, A.; Manzoni, A.
Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers
93/2024 - Conti, P.; Kneifl, J.; Manzoni, A.; Frangi, A.; Fehr, J.; Brunton, S.L.; Kutz, J.N.
VENI, VINDy, VICI - a variational reduced-order modeling framework with uncertainty quantification
94/2024 - Franco, N.R.; Fresca, S.; Tombari, F.; Manzoni, A.
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks
86/2024 - 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
85/2024 - 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
82/2024 - Rosafalco, L.; Conti, P.; Manzoni, A.; Mariani, S.; Frangi, A.
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
83/2024 - Conti, P.; Guo, M.; Manzoni, A.; Frangi, A.; Brunton, S. L.; Kutz, J.N.
Multi-fidelity reduced-order surrogate modelling
78/2024 - Ziarelli, G.; Pagani, S.; Parolini, N.; Regazzoni, F.; Verani, M.
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
71/2024 - Zhang, L.; Pagani, S.; Zhang, J.; Regazzoni, F.
Shape-informed surrogate models based on signed distance function domain encoding
63/2024 - Vitullo, P.; Franco, N.R.; Zunino, P.
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
53/2024 - Caldana, M.; Hesthaven, J. S.
Neural ordinary differential equations for model order reduction of stiff systems
47/2024 - Franco, N.R.; Brugiapaglia, S.
A practical existence theorem for reduced order models based on convolutional autoencoders
40/2024 - Carrara, D.; Regazzoni, F.; Pagani, S.
Implicit neural field reconstruction on complex shapes from scattered and noisy data
32/2024 - Ziarelli, G.; Parolini, N.; Verani, M.
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control
29/2024 - Palummo, A.;, Arnone, E.; Formaggia, L.; Sangalli, L.M.
Functional principal component analysis for incomplete space-time data
25/2024 - Enrico Ballini e Luca Formaggia e Alessio Fumagalli e Anna Scotti e Paolo Zunino
Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
21/2024 - Caldana, M.; Antonietti P. F.; Dede' L.
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
19/2024 - Torzoni, M.; Manzoni, A.; Mariani, S.
A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks
20/2024 - Torzoni, M.; Manzoni, A.; Mariani, S.
Structural health monitoring of civil structures: A diagnostic framework powered by deep metric learning
05/2024 - 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
04/2024 - Torzoni, M.; Tezzele, M.; Mariani, S.; Manzoni, A.; Willcox, K.E.
A digital twin framework for civil engineering structures
02/2024 - Parolini, N.; Poiatti, A.; Vene', J.; Verani, M.
Structure-preserving neural networks in data-driven rheological models
105/2023 - Cicci, L.; Fresca, S.; Guo, M.; Manzoni, A.; Zunino, P.
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
85/2023 - Arnone, E.; De Falco, C.; Formaggia, L.; Meretti, G.; Sangalli, L.M.
Computationally efficient techniques for Spatial Regression with Differential Regularization
68/2023 - Vitullo, P.; Colombo, A.; Franco, N.R.; Manzoni, A.; Zunino, P.
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
66/2023 - 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
67/2023 - Conti, P.; Guo, M.; Manzoni, A.; Hesthaven, J.S.
Multi-fidelity surrogate modeling using long short-term memory networks
40/2023 - Ballini, E.; Chiappa, A.S.; Micheletti, S.
Reducing the Drag of a Bluff Body by Deep Reinforcement Learning
37/2023 - Regazzoni, F.; Pagani, S.; Salvador, M.; Dede’, L.; Quarteroni, A.
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes
34/2023 - Caldana, M.; Antonietti, P. F.; Dede', L.
A Deep Learning algorithm to accelerate Algebraic Multigrid methods in Finite Element solvers of 3D elliptic PDEs
15/2023 - Ragni, A.; Masci, C.; Ieva, F.; Paganoni, A. M.
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach
79/2022 - Antonietti, P. F.; Farenga, N.; Manuzzi, E.; Martinelli, G.; Saverio, L.
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers
69/2022 - Franco, N.R; Manzoni, A.; Zunino, P.
Learning Operators with Mesh-Informed Neural Networks
71/2022 - Calabrò, D.; Lupo Pasini, M.; Ferro, N.; Perotto, S.
A deep learning approach for detection and localization of leaf anomalies
45/2022 - Franco, N.; Fresca, S.; Manzoni, A.; Zunino, P.
Approximation bounds for convolutional neural networks in operator learning