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arXiv · 2210.02184

Rediscovery of Numerical L\"uscher's Formula from the Neural Network

Yu Lu·Yi-Jia Wang·Ying Chen·Jia-Jun Wu
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Abstract

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 L\"uscher's formula to a high precision. The model-independent property of the L\"uscher'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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Yu Lu, Yi-Jia Wang, Ying Chen, Jia-Jun Wu. 2022-10-05. Rediscovery of Numerical L\"uscher's Formula from the Neural Network. https://doi.org/10.1088/1674-1137%2Fad3b9c

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