output
20222025
most citedStudy of phase transition of Potts model with Domain Adversarial Neural Network

9 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

4 papers

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…

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…

cond-mat.stat-mech2022★ 9 cited

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…

nlin.SI2022★ 5 cited

Nonlinear Schrödinger equation on the half-line without a conserved number of solitons

Vincent Caudrelier, Nicolas Crampe, Eric Ragoucy +1

We explore the phenomena of absorption/emission of solitons by an integrable boundary for the nonlinear Schrödinger equation on the half-line. This is based on the investigation of…