DOE OSTI · 3011538
Genetic programming for the nuclear many-body problem: a guide
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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Bakurov, Illya [Michigan State Univ., East Lansing, MI (United States)] (ORCID:000000026458942X), Giuliani, Pablo [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000281450745), Godbey, Kyle [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000306223646), Haut, Nathaniel [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000239896532), Banzhaf, Wolfgang [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000263823245), Nazarewicz, Witold [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000280847425). 2025-10-14. Genetic programming for the nuclear many-body problem: a guide. https://doi.org/10.1088/1361-6471%2Fae0b98
Cite the original work for its findings. Save a collection to share your selection of sources.