Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Francesco Della Santa"'
1
Akademický článek
Automatic Differentiation-Based Multi-Start for Gradient-Based Optimization Methods
Autor: Francesco Della Santa
Publikováno v: Mathematics, Vol 12, Iss 8, p 1201 (2024)
In global optimization problems, diversification approaches are often necessary to overcome the convergence toward local optima. One approach is the multi-start method, where a set of different starting configurations are taken into account to design
Externí odkaz: https://doaj.org/article/0b414b62c34a460b8e75819f5bc770bd
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2
Akademický článek
Graph-Informed Neural Networks for Regressions on Graph-Structured Data
Autor: Stefano Berrone, Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini, Francesco Vaccarino
Publikováno v: Mathematics, Vol 10, Iss 5, p 786 (2022)
In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN). This new architecture is specifically designed for regression tasks on graph-structure
Externí odkaz: https://doaj.org/article/266bb7d2f9c44ffa897c63bb36f8eb84
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3
Akademický článek
Performance Analysis of Multi-Task Deep Learning Models for Flux Regression in Discrete Fracture Networks
Autor: Stefano Berrone, Francesco Della Santa
Publikováno v: Geosciences, Vol 11, Iss 3, p 131 (2021)
In this work, we investigate the sensitivity of a family of multi-task Deep Neural Networks (DNN) trained to predict fluxes through given Discrete Fracture Networks (DFNs), stochastically varying the fracture transmissivities. In particular, detailed
Externí odkaz: https://doaj.org/article/50210fbb44eb41f1ab5d6a87dc2de29b
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5
Performance Analysis of Multi-Task Deep Learning Models for Flux Regression in Discrete Fracture Networks
Autor: Francesco Della Santa, Stefano Berrone
Publikováno v: Geosciences
Volume 11
Issue 3
Geosciences, Vol 11, Iss 131, p 131 (2021)
In this work, we investigate the sensitivity of a family of multi-task Deep Neural Networks (DNN) trained to predict fluxes through given Discrete Fracture Networks (DFNs), stochastically varying the fracture transmissivities. In particular, detailed
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff5c0865dfdea68de862713556061821
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6
Design optimization of renewable energy systems for NZEBs based on deep residual learning
Autor: Maria Ferrara, Alessandro De Gregorio, Enrico Fabrizio, Antonio Mastropietro, Francesco Vaccarino, Francesco Della Santa, Ulderico Fugacci, Matteo Bilardo
Publikováno v: Renewable energy 176 (2021): 590–605. doi:10.1016/j.renene.2021.05.044
info:cnr-pdr/source/autori:Ferrara, Maria; Della Santa, Francesco; Bilardo, Matteo; De Gregorio, Alessandro; Mastropietro, Antonio; Fugacci, Ulderico; Vaccarino, Francesco; Fabrizio, Enrico/titolo:Design optimization of renewable energy systems for NZEBs based on deep residual learning/doi:10.1016%2Fj.renene.2021.05.044/rivista:Renewable energy/anno:2021/pagina_da:590/pagina_a:605/intervallo_pagine:590–605/volume:176
The design of renewable energy systems for Nearly Zero Energy Buildings (NZEB) is a complex optimization problem. In recent years, simulation-based optimization has demonstrated to be able to support the search for optimal design, but improvements to
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bf6f47af8149d8f1bfa8104aa9db702
http://www.scopus.com/record/display.url?eid=2-s2.0-85107069109&origin=inward
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8
Layer-wise relevance propagation for backbone identification in discrete fracture networks
Autor: Francesco Vaccarino, Sandra Pieraccini, Francesco Della Santa, Antonio Mastropietro, Stefano Berrone
Publikováno v: Journal of Computational Science. 55:101458
In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, releva
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a225cdb750c46cd7f11319c75d5951bc
https://doi.org/10.1016/j.jocs.2021.101458
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