  Using the multilayer convolutional neural network (CNN), we can detect the
quantum phases in random electron systems, and phase diagrams of two and higher
dimensional Anderson transitions and quantum percolations as well as disordered
topological systems have been obtained. Here, instead of using CNN to analyze
the wave functions, we analyze the dynamics of wave packets via long short-term
memory network (LSTM). We adopt the quasi-periodic quantum kicked rotors, which
simulate the three and four dimensional Anderson transitions. By supervised
training, we let LSTM extract the features of the time series of wave packet
displacements in localized and delocalized phases. We then simulate the wave
packets in unknown phases and let LSTM classify the time series to localized
and delocalized phases. We compare the phase diagrams obtained by LSTM and
those obtained by CNN.
