2 citations · 4 across the 5 of their papers we have counts for
5 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…
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…
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…
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…
A New Linear Programming Approach and a New Backtracking Strategy for Multiple-Gradient Descent in Multi-Objective Optimization
Francesco Della Santa
In this work, the author presents a novel method for finding descent directions shared by two or more differentiable functions defined on the same unconstrained domain space. Then,…