论文标题
观察深度神经网络如何通过一维量子力学的能量来理解物理
Observing how deep neural networks understand physics through the energy spectrum of one-dimensional quantum mechanics
论文作者
论文摘要
我们研究了神经网络（NNS）如何使用1D量子力学理解物理。在训练NN以准确预测电势的能量特征值之后，我们用它来确认NN从四个不同方面对物理学的理解。受过训练的NN可以预测不同种类潜力的能量特征值，而不是学到的潜力，预测训练期间未使用的颗粒的存在的概率分布，再现未经训练的物理现象，并预测具有未知物质效应的电位能量特征值。这些结果表明，NN可以从实验数据中学习物理定律，预测与培训不同的条件下实验的结果，并预测训练过程中未提供的类型的物理量。由于NNS与人类的理解方式不同，因此它们将是通过补充人类的理解方式来推进物理学的强大工具。
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.
