Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2410.05319 · Journal of Computational Physics

Computing ground states of Bose-Einstein condensation by normalized deep neural network

Weizhu Bao, Zhipeng Chang, Xiaofei Zhao

Published Oct 9, 2024• 10 citations
arXivHTMLPDF
DOI
10.1016/j.jcp.2024.113486

Related papers

Efficient Computation of Bose-Einstein Condensates with Tensor Neural Networks

Qiumei Huang, Bingyi Wang, Yifan Wang +2 more

•Jan 1, 2026•0 citations

Reconfigured continuous normalized gradient flow for computing ground states of Bose-Einstein condensates

Hao Wang, Wenjun Cai, Yongzhong Song +1 more

Computer Physics Communications•Jan 17, 2025•4 citations

Neural Network Solver for Multi-Component Bose Einstein Condensates

John Heropoulos, Seth T. Rittenhouse

Bulletin of the American Physical Society•May 30, 2019•0 citations

Physics-informed neural networks for the quantum droplets in binary Bose–Einstein condensates

Dongshuai Liu, Boris A. Malomed, Wen Zhang

arXiv:2602.04590•Chaos Solitons & Fractals•Feb 4, 2026•2 citations

Theory-guided neural network for studying the ground state of 2D spin-orbit coupled Bose–Einstein condensates

Junyan Kuang, Xiaodong Bai, Wenjuan Du +1 more

Machine learning has been a powerful tool to study various models in many fields, which requires plenty of data to ensure accuracy. It is a challenge to reduce the dependence on the amount of data and ensure that the predictions are both accurate and physical. We develop a theory-guided neural network (TgNN) to explore the ground states of two-component spin–orbit coupled Bose–Einstein condensation in two-dimension. Only randomly selected few data from the twelve ground states as training data, TgNN performs higher accuracy, broader generalization ability and stronger robustness than deep neural network (DNN). Moreover, in the neighborhood of the phase transition where the predictions of DNN have obvious deviations, while TgNN still keeps relative accuracy. TgNN is less dependent on data and exhibits potential for high-dimensional and complicated models. • TgNN accurately predicts all ground states of 2D SOC BEC, which uses only one-tenth of the total data. • TgNN provides higher accuracy, broader generality ability and stronger robustness than DNN. • TgNN has more advantages than DNN in solving critical problems.

Results in Physics•Aug 23, 2024•1 citation

Generation of Bose-Einstein Condensates’ Ground State Through Machine Learning

Xiao Liang, Huan Zhang, Sheng Liu +2 more

Abstract We show that both single-component and two-component Bose-Einstein condensates’ (BECs) ground states can be simulated by a deep convolutional neural network. We trained the neural network via inputting the parameters in the dimensionless Gross-Pitaevskii equation (GPE) and outputting the ground-state wave function. After the training, the neural network generates ground-state wave functions with high precision. We benchmark the neural network for either inputting different coupling strength in the GPE or inputting an arbitrary potential under the infinite double walls trapping potential, and it is found that the ground state wave function generated by the neural network gives the relative chemical potential error magnitude below 10−3. Furthermore, the neural network trained with random potentials shows prediction ability on other types of potentials. Therefore, the BEC ground states, which are continuous wave functions, can be represented by deep convolutional neural networks.

arXiv:1712.10093•Scientific Reports•Oct 30, 2018•12 citations
Machine learning has been a powerful tool to study various models in many fields, which requires plenty of data to ensure accuracy. It is a challenge to reduce the dependence on the amount of data and ensure that the predictions are both accurate and physical. We develop a theory-guided neural network (TgNN) to explore the ground states of two-component spin–orbit coupled Bose–Einstein condensation in two-dimension. Only randomly selected few data from the twelve ground states as training data, TgNN performs higher accuracy, broader generalization ability and stronger robustness than deep neural network (DNN). Moreover, in the neighborhood of the phase transition where the predictions of DNN have obvious deviations, while TgNN still keeps relative accuracy. TgNN is less dependent on data and exhibits potential for high-dimensional and complicated models. • TgNN accurately predicts all ground states of 2D SOC BEC, which uses only one-tenth of the total data. • TgNN provides higher accuracy, broader generality ability and stronger robustness than DNN. • TgNN has more advantages than DNN in solving critical problems.
Abstract We show that both single-component and two-component Bose-Einstein condensates’ (BECs) ground states can be simulated by a deep convolutional neural network. We trained the neural network via inputting the parameters in the dimensionless Gross-Pitaevskii equation (GPE) and outputting the ground-state wave function. After the training, the neural network generates ground-state wave functions with high precision. We benchmark the neural network for either inputting different coupling strength in the GPE or inputting an arbitrary potential under the infinite double walls trapping potential, and it is found that the ground state wave function generated by the neural network gives the relative chemical potential error magnitude below 10−3. Furthermore, the neural network trained with random potentials shows prediction ability on other types of potentials. Therefore, the BEC ground states, which are continuous wave functions, can be represented by deep convolutional neural networks.