  Deep learning, accounting for the use of an elaborate neural network, has
recently been developed as an efficient and powerful tool to solve diverse
problems in physics and other sciences. In the present work, we propose a novel
learning method based on a hybrid network integrating two different kinds of
neural networks: Long Short-Term Memory(LSTM) and Deep Residual
Network(ResNet), in order to overcome the difficulty met in numerically
simulating strongly-oscillating dynamical evolutions of physical systems. By
taking the dynamics of Bose-Einstein condensates in a double-well potential as
an example, we show that our new method makes a high efficient pre-learning and
a high-fidelity prediction about the whole dynamics. This benefits from the
advantage of the combination of the LSTM and the ResNet and is impossibly
achieved by a single network in the case of direct learning. Our method can be
applied for simulating complex cooperative dynamics in a system with fast
multiple-frequency oscillations with the aid of auxiliary spectrum analysis.
