Revisiting the double-well problem by deep learning with a hybrid network

• URL: http://arxiv.org/abs/2104.14657v1
• Date: Sun, 25 Apr 2021 07:51:43 GMT
• Title: Revisiting the double-well problem by deep learning with a hybrid network
• Authors: Shurui Li, Jianqin Xu and Jing Qian
• Abstract summary: We propose a novel hybrid network which integrates two different kinds of neural networks: LSTM and ResNet. Such a hybrid network can be applied for solving cooperative dynamics in a system with fast spatial or temporal modulations.
• Score: 7.308730248177914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solving physical problems by deep learning is accurate and efficient mainly accounting for the use of an elaborate neural network. We propose a novel hybrid network which integrates two different kinds of neural networks: LSTM and ResNet, in order to overcome the difficulty met in solving strongly-oscillating dynamics of the system's time evolution. By taking the double-well model as an example we show that our new method can benefit from a pre-learning and verification of the periodicity of frequency by using the LSTM network, simultaneously making a high-fidelity prediction about the whole dynamics of system with ResNet, which is impossibly achieved in the case of single network. Such a hybrid network can be applied for solving cooperative dynamics in a system with fast spatial or temporal modulations, promising for realistic oscillation calculations under experimental conditions.

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