Universal Models for Heavy-Ion Fusion Cross Section Above-Barrier

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Lombardo:2023:EPJ,

• author = "Ivano Lombardo and Daniele Dell'Aquila and Brunilde Gnoffo and Luigi Redigolo and Francesco Porto and Marco Russo",
• title = "Universal Models for Heavy-Ion Fusion Cross Section Above-Barrier",
• journal = "EPJ Web of Conferences",
• year = "2023",
• volume = "290",
• pages = "Article Number: 02017",
• keywords = "genetic algorithms, genetic programming, BP",
• ISSN = "2100-014X",
• URL = "https://www.epj-conferences.org/articles/epjconf/pdf/2023/16/epjconf_eunpc2023_02017.pdf",
• DOI = "doi:10.1051/epjconf/202329002017",
• size = "4 pages",
• abstract = "The paper discusses a recent re-investigation of a large body of heavy-ion fusion cross section data with the aim of deriving a simple phenomenological model able to describe data from the Coulomb barrier up to the onset of nuclear multifragmentation. To this end, we adopted two complementary approaches: a first universal phenomenological model was derived exploiting a novel artificial intelligence tool for the formal modeling of large datasets. This tool is capable of advanced feature selection and is ideal to drive the discovery process even using traditional methods. A second phenomenological model was derived using a sum-of-difference approach and achieved an unprecedented accuracy in describing above-barrier fusion excitation functions data. Future perspectives and opportunities arising from the present models are also discussed in the text.",
• notes = "Section P2 Nuclear Structure, Spectroscopy and Dynamics",

}

Genetic Programming entries for Ivano Lombardo Daniele Dell'Aquila Brunilde Gnoffo Luigi Redigolo Francesco Porto Marco Russo

Citations