7 papers
Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
Francesco Della Santa, Sandra Pieraccini, Maria Strazzullo
This work addresses the accurate and efficient simulation of physical phenomena governed by parametric Partial Differential Equations (PDEs) characterized by varying boundary condi…
A Recipe for Learning Variably Scaled Kernels via Discontinuous Neural Networks
Gianluca Audone, Francesco Della Santa, Emma Perracchione +1
The efficacy of interpolating via Variably Scaled Kernels (VSKs) is known to be dependent on the definition of a proper scaling function, but no numerical recipes to construct it a…
Graph-Instructed Neural Networks for Sparse Grid-Based Discontinuity Detectors
Francesco Della Santa, Sandra Pieraccini
In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function. This approach leverages Graph-Instructed Neural Networks (GINNs)…
Automated Detection of Sport Highlights from Audio and Video Sources
Francesco Della Santa, Morgana Lalli
This study presents a novel Deep Learning-based and lightweight approach for the automated detection of sports highlights (HLs) from audio and video sources. HL detection is a key…
Edge-Wise Graph-Instructed Neural Networks
Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini +1
The problem of multi-task regression over graph nodes has been recently approached through Graph-Instructed Neural Network (GINN), which is a promising architecture belonging to th…
GradINN: Gradient Informed Neural Network
Filippo Aglietti, Francesco Della Santa, Andrea Piano +1
We propose Gradient Informed Neural Networks (GradINNs), a methodology inspired by Physics Informed Neural Networks (PINNs) that can be used to efficiently approximate a wide range…