| CONDENSED MATTER: STRUCTURAL, MECHANICAL, AND THERMAL PROPERTIES |
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Three-dimensional ResNet for efficient prediction of ground state phases in multicomponent dipolar spinor BECs |
| Chengji Liao(廖承继)1, Tiantian Li(李甜甜)1,†, Xiao-Dong Bai(柏小东)2, and Yunbo Zhang(张云波)3 |
1 School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411100, China;
2 College of Physics and Hebei Key Laboratory of Photophysics Research and Application, Hebei Normal University, Shijiazhuang 050024, China;
3 Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, China |
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Abstract Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems; however, its application to intricate three-dimensional (3D) systems remains relatively underexplored. In this study, we introduce a 3D residual network (3D ResNet) framework based on 3D convolutional neural networks (3D CNN) to predict ground states phases in 3D dipolar spinor Bose-Einstein condensates (BECs). Our results show that the 3D ResNet framework predicts ground states with high accuracy and efficiency across a broad parameter space. To enhance phase transition predictions, we incorporate data augmentation techniques, leading to a notable improvement in the model's performance. The method is further validated in more complex scenarios, particularly when transverse magnetic fields are introduced. Compared to conventional imaginary-time evolution methods (ITEM), the 3D ResNet drastically reduces computational costs, offering a rapid and scalable solution for complex 3D multi-parameter nonlinear systems.
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Received: 24 February 2025
Revised: 28 March 2025
Accepted manuscript online: 02 April 2025
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PACS:
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67.85.Fg
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(Multicomponent condensates; spinor condensates)
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03.75.Mn
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(Multicomponent condensates; spinor condensates)
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05.30.Rt
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(Quantum phase transitions)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11904309 and 12305015), the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ5528), and the Natural Science Foundation of Hebei Province, China (Grant No. A2024205027). |
Corresponding Authors:
Tiantian Li
E-mail: ttli@xtu.edu.cn
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Cite this article:
Chengji Liao(廖承继), Tiantian Li(李甜甜), Xiao-Dong Bai(柏小东), and Yunbo Zhang(张云波) Three-dimensional ResNet for efficient prediction of ground state phases in multicomponent dipolar spinor BECs 2025 Chin. Phys. B 34 076701
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