Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Dell'Aquila:jpG,
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author = "Daniele Dell'Aquila and Brunilde Gnoffo and
Ivano Lombardo and Francesco Porto and Marco Russo",
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title = "Modeling Heavy-Ion Fusion Cross Section Data via a
Novel Artificial Intelligence Approach",
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journal = "Journal of Physics G: Nuclear and Particle Physics",
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year = "2022",
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volume = "50",
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number = "1",
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pages = "015101",
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month = nov,
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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",
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publisher = "IOP Publishing",
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URL = "https://arxiv.org/abs/2203.10367",
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URL = "http://iopscience.iop.org/article/10.1088/1361-6471/ac9ad1",
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DOI = "doi:10.1088/1361-6471/ac9ad1",
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size = "22 pages",
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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.",
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notes = "Brain Project",
- }
Genetic Programming entries for
Daniele Dell'Aquila
Brunilde Gnoffo
Ivano Lombardo
Francesco Porto
Marco Russo
Citations