论文标题
通过机器学习发现Gellmann-Okubo公式
Discover the GellMann-Okubo formula with machine learning
论文作者
论文摘要
机器学习是一种新颖而强大的技术，已在各种科学主题中广泛使用。我们演示了一种基于机器学习的方法，该方法由一组受物理学启发的通用指标和规则。利用符号回归技术，我们成功地重新发现了Gellmann Okubo公式。这种方法可以有效地找到用户定义的可观察到的明确解决方案，并很容易扩展到外来的强子光谱。
Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical constraints, such as dimension identity, symmetry and generalization, we succeed to rediscover the GellMann Okubo formula using a technique of symbolic regression. This approach can effectively find explicit solutions among user-defined observable, and easily extend to study on exotic hadron spectrum.
