40 citations · 68 across the 13 of their papers we have counts for
13 papers
Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models
Shanshan Wang, Dian Xu, Jianmin Shen +3
Predicting critical phenomena from limited labeled data remains a challenging task in statistical physics. As percolation theory provides a canonical model for phase transitions wi…
Neural network learning of multi-scale and discrete temporal features in directed percolation
Feng Gao, Jianmin Shen, Shanshan Wang +2
Neural network methods are increasingly applied to solve phase transition problems, particularly in identifying critical points in non-equilibrium phase transitions, offering more…
Identifying Ising and percolation phase transitions based on KAN method
Dian Xu, Shanshan Wang, Wei Li +3
Modern machine learning, grounded in the Universal Approximation Theorem, has achieved significant success in the study of phase transitions in both equilibrium and non-equilibrium…
The tricritical point of tricritical directed percolation is determined based on neural network
Feng Gao, Jianmin Shen, Shanshan Wang +2
In recent years, neural networks have increasingly been employed to identify critical points of phase transitions. For the tricritical directed percolation model, its steady-state…
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…
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…
Identifying percolation phase transitions with unsupervised learning based on largest clusters
Dian Xu, Shanshan Wang, Weibing Deng +3
The application of machine learning in the study of phase transitions has achieved remarkable success in both equilibrium and non-equilibrium systems. It is widely recognized that…
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…
Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks
Yanyang Wang, Wei Li, Feiyi Liu +1
Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and no…
Study of phase transition of Potts model with Domain Adversarial Neural Network
Xiangna Chen, Feiyi Liu, Shiyang Chen +5
A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only nee…
Transfer learning of phase transitions in percolation and directed percolation
Jianmin Shen, Feiyi Liu, Shiyang Chen +6
The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural…
Machine learning of pair-contact process with diffusion
Jianmin Shen, Wei Li, Shengfeng Deng +3
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition.…
Supervised and unsupervised learning of directed percolation
Jianmin Shen, Wei Li, Shengfeng Deng +1
Machine learning (ML) has been well applied to studying equilibrium phase transition models, by accurately predicating critical thresholds and some critical exponents. Difficulty w…