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Discovering Nuclear Models from Symbolic Machine Learning
April 23, 2024, 4:44 a.m. | Jose M. Munoz, Silviu M. Udrescu, Ronald F. Garcia Ruiz
cs.LG updates on arXiv.org arxiv.org
Abstract: Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic …
abstract arxiv behavior challenge cs.ai cs.lg explore however machine machine learning novel nuclear nucl-ex nucl-th type unified model
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