Genetic programming for the nuclear many-body problem: a guide

Created by W.Langdon from gp-bibliography.bib Revision:1.8972

@Article{Bakurov:2024rmu,

• author = "Illya Bakurov and Pablo Giuliani and Kyle Godbey and Nathaniel Haut and Wolfgang Banzhaf and Witold Nazarewicz",
• title = "Genetic programming for the nuclear many-body problem: a guide",
• journal = "Journal of Physics G: Nuclear and Particle Physics",
• year = "2025",
• volume = "52",
• number = "10",
• pages = "102001",
• keywords = "genetic algorithms, genetic programming,nuclear many-body problem, reduced order models, dimensionality reduction",
• eprint = "2406.04279",
• archiveprefix = "arXiv",
• primaryclass = "nucl-th",
• URL = "https://iopscience.iop.org/article/10.1088/1361-6471/ae0b98",
• URL = "https://inspirehep.net/literature/2795332",
• URL = "https://arxiv.org/abs/2406.04279",
• DOI = "10.1088/1361-6471/ae0b98",
• abstract = "Genetic Programming (GP) is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use GP to develop surrogate models to mitigate the computational costs of modeling atomic nuclei with ever increasing complexity. The computational burden escalates when uncertainty quantification is pursued, or when observables must be globally computed for thousands of nuclei. By studying three models in which the mean field depends on the total particle density self-consistently, we show that by constructing reduced order models supported by GP one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy.",
• notes = "bakurov2025geneticprogrammingnuclearmanybody",

}

Genetic Programming entries for Illya Bakurov Pablo Giuliani Kyle Godbey Nathaniel Haut Wolfgang Banzhaf Witold Nazarewicz

Citations