5 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…
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…
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…