Approach the Gell-Mann-Okubo Formula with Machine Learning

Machine learning is a novel and powerful technology has been widely used in various science topics. We demonstrate machine-learning-based approach built by set of general metrics rules inspired physics. Taking advantages physical constraints, such as dimension identity, symmetry generalization, we succeed to the Gell-Mann–Okubo formula using technique symbolic regression. This can effectively find explicit solutions among user-defined observables, be extensively applied studying exotic hadron spectrum.