We investigated how neural networks (NNs) understand physics using one-dimensional 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 a different potential than the one learned, focus on minima and maxima of a potential, predict the probability distribution of the existence of particles not used during training, and reproduce untrained physical phenomena. These results show that NNs can learn the laws of physics from only a limited set of data, predict the results of experiments under conditions different from those used for training, and predict physical quantities of types not provided during training. Since NNs understand physics through a different path than humans take, and by complementing the human way of understanding, they will be a powerful tool for advancing physics.
翻译:我们调查了神经网络如何利用一维量子力学来理解物理学。在培训NN来准确预测潜在能源的精度值之后,我们用它来证实NN对物理的四个不同方面的了解。受过训练的NN可以预测与所学潜力不同的能量精度值,关注潜力的微量值和最大值,预测在训练期间没有使用的粒子的概率分布,并复制未经训练的物理现象。这些结果显示NNN只能从有限的一组数据中学习物理定律,预测在不同于培训条件下的实验结果,预测培训期间没有提供的类型的物理数量。 由于NNN通过不同于人类的路径理解物理学,并通过补充人类的理解方式,它们将成为推进物理学的强大工具。