通过一维量子力学的能谱观察神经网络对物理的理解有多深
Observing how deep neural networks understand physics through the energy spectrum of one-dimensional quantum mechanics
摘要：
我们研究神经网络（NN）如何使用一维量子力学理解物理。在训练了一个神经网络准确地从势预测能量本征值之后，我们用它从四个不同的方面来确认神经网络对物理的理解。训练好的神经网络可以预测不同种类的势能的能量本征值，预测训练过程中未使用的粒子存在的概率分布，再现未经训练的物理现象，并预测具有未知物质效应的势能的能量本征值。这些结果表明，神经网络可以从实验数据中学习物理规律，预测在与用于训练的条件不同的条件下的实验结果，并预测在训练期间未提供的类型的物理量。由于神经网络理解物理的方式与人类不同，它们将通过补充人类的理解方式，成为推进物理学的强大工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
