57 citations · 618 across the 33 of their papers we have counts for
3 papers · 1 filter
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Anna Ivagnes, Nicola Demo, Gianluigi Rozza
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation…
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Dario Coscia, Laura Meneghetti, Nicola Demo +2
Convolutional Neural Network (CNN) is one of the most important architectures in deep learning. The fundamental building block of a CNN is a trainable filter, represented as a disc…
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Laura Meneghetti, Nicola Demo, Gianluigi Rozza
As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields i…