  We show that both single-component and two-component Bose-Einstein
condensates' (BECs) ground states can be simulated by deep convolutional neural
networks of the same structure. We trained the neural network via inputting the
coupling strength in the dimensionless Gross-Pitaevskii equation (GPE) and
outputting the ground state wave-function. After training, the neural network
generates ground states faster than the method of imaginary time evolution,
while the relative mean-square-error between predicted states and original
states is in the magnitude between $10^{-5}$ and $10^{-4}$. We compared the
eigen-energies based on predicted states and original states, it is shown that
the neural network can predict eigen-energies in high precisions. Therefore,
the BEC ground states, which are continuous wave-functions, can be represented
by deep convolution neural networks.
