1. Department of Physics, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China 2. School of Physics and Astronomy, and Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai 200240, China 3. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China 4. Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
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 highly 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 with a single network in the case of direct learning. Our method can be applied for simulating complex cooperative dynamics in a system with fast multiplefrequency oscillations with the aid of auxiliary spectrum analysis.
. [J]. Frontiers of Physics, 2022, 17(2): 22504.
Shurui Li, Jianqin Xu, Jing Qian, Weiping Zhang. Revisiting the dynamics of Bose–Einstein condensates in a double well by deep learning with a hybrid network. Front. Phys. , 2022, 17(2): 22504.
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