57 citations · 618 across the 33 of their papers we have counts for
5 papers · 1 filter
A Dimensionality Reduction Approach for Convolutional Neural Networks
Laura Meneghetti, Nicola Demo, Gianluigi Rozza
The focus of this paper is the application of classical model order reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks. We…
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
Nicola Demo, Maria Strazzullo, Gianluigi Rozza
In this work we propose an extension of physics informed supervised learning strategies to parametric partial differential equations. Indeed, even if the latter are indisputably us…
A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems
Francesco Andreuzzi, Nicola Demo, Gianluigi Rozza
Dynamic mode decomposition (DMD) has recently become a popular tool for the non-intrusive analysis of dynamical systems. Exploiting Proper Orthogonal Decomposition (POD) as a dimen…
The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations
Davide Papapicco, Nicola Demo, Michele Girfoglio +2
Models with dominant advection always posed a difficult challenge for projection-based reduced order modelling. Many methodologies that have recently been proposed are based on the…
Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
Nicola Demo, Marco Tezzele, Andrea Mola +1
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a…