Learning High-Order Interactions via Targeted Pattern Search. - Michela Carlotta Massi; Nicola R. Franco; Francesca Ieva; Andrea Manzoni; Anna Maria Paganoni; Paolo Zunino (CoRR, 2021)

Learning Operators with Mesh-Informed Neural Networks. - Nicola Rares Franco; Andrea Manzoni; Paolo Zunino (CoRR, 2022)

A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations. - Nicola R. Franco; Andrea Manzoni; Paolo Zunino (Math. Comput., 2022)

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)

Nonlinear model order reduction for problems with microstructure using mesh informed neural networks. - Piermario Vitullo; Alessio Colombo; Nicola Rares Franco; Andrea Manzoni; Paolo Zunino (CoRR, 2023)

Deep learning based reduced order modeling of Darcy flow systems with local mass conservation. - Wietse M. Boon; Nicola R. Franco; Alessio Fumagalli; Paolo Zunino (CoRR, 2023)

Approximation bounds for convolutional neural networks in operator learning. - Nicola Rares Franco; Stefania Fresca; Andrea Manzoni; Paolo Zunino (Neural Networks, 2023)

Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces. - Nicola Rares Franco; Andrea Manzoni; Paolo Zunino (J. Sci. Comput., 2023)

A practical existence theorem for reduced order models based on convolutional autoencoders. - Nicola Rares Franco; Simone Brugiapaglia (CoRR, 2024)

Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy. - Piermario Vitullo; Nicola Rares Franco; Paolo Zunino (CoRR, 2024)

Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction. - Nicola Rares Franco; Andrea Manzoni; Paolo Zunino; Jan S. Hesthaven (CoRR, 2024)

Measurability and continuity of parametric low-rank approximation in Hilbert spaces: linear operators and random variables. - Nicola Rares Franco (CoRR, 2024)

Neural network solvers for parametrized elasticity problems that conserve linear and angular momentum. - Wietse M. Boon; Nicola R. Franco; Alessio Fumagalli (CoRR, 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., 2024)

On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields. - Nicola Rares Franco; Daniel Fraulin; Andrea Manzoni; Paolo Zunino (Adv. Comput. Math., 2024)

Numerical Solution of Mixed-Dimensional PDEs Using a Neural Preconditioner. - Nunzio Dimola; Nicola Rares Franco; Paolo Zunino (CoRR, 2025)

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective. - Simone Brivio; Nicola Rares Franco (CoRR, 2025)