Zobrazeno 1 - 10
of 25
pro vyhledávání: '"D'Inverno, Giuseppe Alessio"'
Autor:
Varbella, Anna, Briens, Damien, Gjorgiev, Blazhe, D'Inverno, Giuseppe Alessio, Sansavini, Giovanni
The energy transition is driving the integration of large shares of intermittent power sources in the electric power grid. Therefore, addressing the AC optimal power flow (AC-OPF) effectively becomes increasingly essential. The AC-OPF, which is a fun
Externí odkaz:
http://arxiv.org/abs/2410.04818
Autor:
D'Inverno, Giuseppe Alessio, Moradizadeh, Saeid, Salavatidezfouli, Sajad, Africa, Pasquale Claudio, Rozza, Gianluigi
The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs)
Externí odkaz:
http://arxiv.org/abs/2410.03802
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 respect to the signal(s) incoming. We study the characterization of the
Externí odkaz:
http://arxiv.org/abs/2402.06768
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 asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive
Externí odkaz:
http://arxiv.org/abs/2401.14557
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 passing mechanism, GNNs have gained increasing popularity due to their intui
Externí odkaz:
http://arxiv.org/abs/2401.12362
Autor:
Bucarelli, Maria Sofia, D'Inverno, Giuseppe Alessio, Bianchini, Monica, Scarselli, Franco, Silvestri, Fabrizio
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 gradient descent. This search for an appropriate description, both anal
Externí odkaz:
http://arxiv.org/abs/2401.03824
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 gained increasing popularity for their intuitive formulation, which is clos
Externí odkaz:
http://arxiv.org/abs/2307.00134
Autor:
Beddar-Wiesing, Silvia, D'Inverno, Giuseppe Alessio, Graziani, Caterina, Lachi, Veronica, Moallemy-Oureh, Alice, Scarselli, Franco, Thomas, Josephine Maria
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 the one hand, it has been proven that GNNs are as powerful as the Weis
Externí odkaz:
http://arxiv.org/abs/2210.03990
Publikováno v:
In Neural Networks January 2025 181
Publikováno v:
In Neurocomputing 1 January 2025 611