| Approach the Gell-Mann–Okubo Formula with Machine Learning |
| |
| 作者姓名： | 张振宇 马瑞 胡继峰 王倩 |
| |
| 作者单位： | 1. Guangdong Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter,South China Normal University;2. Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Southern Nuclear Science Computing Center,South China Normal University |
| |
| 基金项目： | supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2020B0301030008)；;the National Natural Science Foundation of China (Grant No. 12035007)；;supported by the National Natural Science Foundation of China (Grant No. 12070131001)；;the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the funds provided to the Sino-German Collaborative Research Center TRR110 “Symmetries and the Emergence of Structure in QCD” (Grant No. DFG Project-ID 196253076-TRR 110)； |
| |
| 摘 要： | 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 approach the Gell-Mann-Okubo formula using a technique of symbolic regression.This approach can effectively find explicit solutions among user-defined observables,and can be...
|
|
|
