We investigate how neural networks (NNs) understand physics using 1D quantum mechanics. After training an NN to accurately predict energy eigenvalues from potentials, we used it to confirm the NN's understanding of physics from four different aspects. The trained NN could predict energy eigenvalues of different kinds of potentials than the ones learned, predict the probability distribution of the existence of particles not used during training, reproduce untrained physical phenomena, and predict the energy eigenvalues of potentials with an unknown matter effect. These results show that NNs can learn physical laws from experimental data, predict the results of experiments under conditions different from those used for training, and predict physical quantities of types not provided during training. Because NNs understand physics in a different way than humans, they will be a powerful tool for advancing physics by complementing the human way of understanding.

翻译：我们调查神经网络(NNs)如何利用 1D 量子力学来理解物理。在培训NN(NNs)来准确预测潜在能源的精度值之后,我们用它来证实NN(NN)对物理有四个不同方面的了解。受过训练的NN(NN)可以预测不同潜力种类的能量精度值,预测培训期间没有使用的粒子的概率分布,复制未经训练的物理现象,预测潜力的能量精度值,产生未知物质效应。这些结果显示NNN(N)可以从实验数据中学习物理法则,预测在与培训不同的条件下进行实验的结果,预测培训期间没有提供的物理种类的数量。由于NNS(N)理解物理学的方式不同于人类,它们将成为通过补充人类理解的方式推进物理的有力工具。