40 citations · 68 across the 13 of their papers we have counts for
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physics.comp-ph2024★ 2 cited
Learning phase transitions by siamese neural network
Jianmin Shen, Shiyang Chen, Feiyi Liu +2
The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised l…
physics.comp-ph2023
Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model
Xiangna Chen, Feiyi Liu, Weibing Deng +5
Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the…
physics.comp-ph2023
Machine learning of (1+1)-dimensional directed percolation based on raw and shuffled configurations
Shen Jianmin, Wang Shanshan, Li Wei +6
Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML t…