Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach

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@Article{Dell'Aquila:jpG,

• author = "Daniele Dell'Aquila and Brunilde Gnoffo and Ivano Lombardo and Francesco Porto and Marco Russo",
• title = "Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach",
• journal = "Journal of Physics G: Nuclear and Particle Physics",
• year = "2022",
• volume = "50",
• number = "1",
• pages = "015101",
• month = nov,
• keywords = "genetic algorithms, genetic programming, BP, ANN, AI, heavy ion fusion, excitation function, artificial intelligence in nuclear data, Nuclear Experiment (nucl-ex), Nuclear Theory (nucl-th), FOS: Physical sciences, FOS: Physical sciences",
• publisher = "IOP Publishing",
• URL = "https://arxiv.org/abs/2203.10367",
• URL = "http://iopscience.iop.org/article/10.1088/1361-6471/ac9ad1",
• DOI = "10.1088/1361-6471/ac9ad1",
• size = "22 pages",
• abstract = "We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.",
• notes = "Brain Project",

}

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

Citations