23 citations · 168 across the 23 of their papers we have counts for
23 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…
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…
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…
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…
The characteristics of cycle-nodes-ratio and its application to network classification
Wenjun Zhang, Wei Li, Weibing Deng
Cycles, which can be found in many different kinds of networks, make the problems more intractable, especially when dealing with dynamical processes on networks. On the contrary, t…