Genetic Programming for the Nuclear Many-Body Problem: a Guide
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915338489167872 |
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| author | Bakurov, Illya Giuliani, Pablo Godbey, Kyle Haut, Nathaniel Banzhaf, Wolfgang Nazarewicz, Witold |
| author_facet | Bakurov, Illya Giuliani, Pablo Godbey, Kyle Haut, Nathaniel Banzhaf, Wolfgang Nazarewicz, Witold |
| contents | Genetic Programming is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use Genetic Programming 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 Genetic Programming one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04279 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Genetic Programming for the Nuclear Many-Body Problem: a Guide Bakurov, Illya Giuliani, Pablo Godbey, Kyle Haut, Nathaniel Banzhaf, Wolfgang Nazarewicz, Witold Nuclear Theory Genetic Programming is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use Genetic Programming 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 Genetic Programming one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy |
| title | Genetic Programming for the Nuclear Many-Body Problem: a Guide |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2406.04279 |