5 citations · 6 across the 4 of their papers we have counts for
5 papers
Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction
Nicola Rares Franco, Andrea Manzoni, Paolo Zunino +1
We develop a novel deep learning technique, termed Deep Orthogonal Decomposition (DOD), for dimensionality reduction and reduced order modeling of parameter dependent partial diffe…
VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
Paolo Conti, Jonas Kneifl, Andrea Manzoni +4
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent thi…
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…
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Piermario Vitullo, Alessio Colombo, Nicola Rares Franco +2
Many applications in computational physics involve approximating problems with microstructure, characterized by multiple spatial scales in their data. However, these numerical solu…