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cond-mat.dis-nn2025
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…
cond-mat.stat-mech2025
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…
cond-mat.stat-mech2025
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…