从神经网络重新发现吕歇尔数值公式*

IF 3.6 2区物理与天体物理 Q1 PHYSICS, NUCLEAR

中国物理C Pub Date : 2024-06-30 DOI:10.1088/1674-1137/ad3b9c

Yu Lu, 宇陆, Yi-Jia Wang, 一佳王, Ying Chen, 莹陈, Jia-Jun Wu and 佳俊吴

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引用次数: 0

摘要

我们发现，通过连续空间的相移来预测离散空间的频谱，神经网络可以高精度地再现数值吕歇尔公式。神经网络的泛化能力自然而然地实现了吕歇尔公式与模型无关的特性。这显示了神经网络在提取与模型无关的量之间关系的巨大潜力，而这种数据驱动的方法可以极大地促进发现错综复杂的数据背后的物理原理。

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Rediscovery of numerical Lüscher's formula from the neural network*

We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical Lüscher's formula to a high precision. The model-independent property of the Lüscher's formula is naturally realized by the generalizability of the neural network. This exhibits the great potential of the neural network to extract model-independent relation between model-dependent quantities, and this data-driven approach could greatly facilitate the discovery of the physical principles underneath the intricate data.

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来源期刊

中国物理C 物理-物理：核物理

CiteScore

6.50

自引率

8.30%

发文量

8976

审稿时长

1.3 months

期刊介绍： Chinese Physics C covers the latest developments and achievements in the theory, experiment and applications of: Particle physics; Nuclear physics; Particle and nuclear astrophysics; Cosmology; Accelerator physics. The journal publishes original research papers, letters and reviews. The Letters section covers short reports on the latest important scientific results, published as quickly as possible. Such breakthrough research articles are a high priority for publication. The Editorial Board is composed of about fifty distinguished physicists, who are responsible for the review of submitted papers and who ensure the scientific quality of the journal. The journal has been awarded the Chinese Academy of Sciences ‘Excellent Journal’ award multiple times, and is recognized as one of China''s top one hundred key scientific periodicals by the General Administration of News and Publications.