8 citations · 10 across the 14 of their papers we have counts for
16 papers
Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field
Federico Tamburlin, Giovanni Canali, Giuseppe Alessio D'Inverno +3
This work presents a differentiable framework for the parametrization and shape optimization of industrial CAD geometries represented by multi-patch NURBS surfaces. The method enab…
From Well-Posed Inversion to Learning Design: Physics- Informed Neural Estimation for Autonomic Regulation
Sara Nour Sadoun, Giuseppe Alessio D'Inverno, Francois Cottin +2
Learning-based and physics-informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic…
Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection
Gabriel Masella, Giuseppe Alessio D'Inverno, Max Goldsmith +1
The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. This work introduces a Graph N…
On Task Vectors and Gradients
Luca Zhou, Daniele Solombrino, Donato Crisostomi +4
Task arithmetic has emerged as a simple yet powerful technique for model merging, enabling the combination of multiple finetuned models into one. Despite its empirical success, a c…
Revisiting Deep Information Propagation: Fractal Frontier and Finite-size Effects
Giuseppe Alessio D'Inverno, Zhiyuan Hu, Leo Davy +3
Information propagation characterizes how input correlations evolve across layers in deep neural networks. This framework has been well studied using mean-field theory, which assum…
Data-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares
Aurora Poggi, Giuseppe Alessio D'Inverno, Hjalmar Brismar +3
Data-driven discovery of dynamics in biological systems allows for better observation and characterization of processes, such as calcium signaling in cell culture. Recent advanceme…
Surrogate models for diffusion on graphs via sparse polynomials
Giuseppe Alessio D'Inverno, Kylian Ajavon, Simone Brugiapaglia
Diffusion kernels over graphs have been widely utilized as effective tools in various applications due to their ability to accurately model the flow of information through nodes an…
Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow
Anna Varbella, Damien Briens, Blazhe Gjorgiev +3
We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions. Unlik…
Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
Giuseppe Alessio D'Inverno, Saeid Moradizadeh, Sajad Salavatidezfouli +2
The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysi…
Product of Tensors and Description of Networks
Luca Chiantini, Giuseppe Alessio D'Inverno, Sara Marziali
Any kind of network can be naturally represented by a Directed Acyclic Graph (DAG); additionally, activation functions can determine the reaction of each node of the network with r…
A topological description of loss surfaces based on Betti Numbers
Maria Sofia Bucarelli, Giuseppe Alessio D'Inverno, Monica Bianchini +2
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on g…
Comparison of Reservoir Computing topologies using the Recurrent Kernel approach
Giuseppe Alessio D'Inverno, Jonathan Dong
Reservoir Computing (RC) has become popular in recent years thanks to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptot…
VC dimension of Graph Neural Networks with Pfaffian activation functions
Giuseppe Alessio D'Inverno, Monica Bianchini, Franco Scarselli
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion; based on a message passin…
Generalization Limits of Graph Neural Networks in Identity Effects Learning
Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gaine…
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
Silvia Beddar-Wiesing, Giuseppe Alessio D'Inverno, Caterina Graziani +4
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On…
On the approximation capability of GNNs in node classification/regression tasks
Giuseppe Alessio D'Inverno, Monica Bianchini, Maria Lucia Sampoli +1
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the…