  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.
