7 citations · 14 across the 5 of their papers we have counts for
3 papers · 1 filter
Recurrent Deep Kernel Learning of Dynamical Systems
Nicolò Botteghi, Paolo Motta, Andrea Manzoni +2
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy…
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
Nicola Rares Franco, Daniel Fraulin, Andrea Manzoni +1
Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tac…
Learning High-Order Interactions via Targeted Pattern Search
Michela C. Massi, Nicola R. Franco, Francesca Ieva +3
Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent fro…