论文标题
从神经网络中重新发现数值吕瑟尔的公式
Rediscovery of Numerical Lüscher's Formula from the Neural Network
论文作者
论文摘要
我们表明，通过预测连续空间相移的离散空间中的频谱，神经网络可以显着将数值吕瑟尔的公式重现为高精度。 Lüscher公式的模型无关性能自然是通过神经网络的普遍性实现的。这表现出神经网络在模型依赖性数量之间提取模型无关的关系的巨大潜力，而这种数据驱动的方法可以极大地促进在复杂数据下方发现的物理原理。
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.
