Study of phase transition of Potts model with Domain Adversarial Neural Network
arXiv:2209.03572 · doi:10.1016/j.physa.2023.128666
Abstract
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 need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost.
arXiv comment30 pages, 36 figures
References in corpus (30)
- Machine learning and the physical sciences
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Machine learning phases of matter
- A high-bias, low-variance introduction to Machine Learning for physicists
- Learning phase transitions by confusion
- Discovering Phase Transitions with Unsupervised Learning
- Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
- Distance-based Protein Folding Powered by Deep Learning
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Machine Learning Topological States
- Domain-Adversarial Neural Networks
- Detection of phase transition via convolutional neural network
- Unsupervised machine learning account of magnetic transitions in the Hubbard model
- Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
- Machine learning in nuclear materials research
- Logarithmic Corrections and Finite-Size Scaling in the Two-Dimensional 4-State Potts Model
- Identifying Quantum Phase Transitions with Adversarial Neural Networks
- Machine learning of phase transitions in the percolation and XY models
- Applications of neural networks to the studies of phase transitions of two-dimensional Potts models
- Unsupervised learning of topological phase transitions using Calinski-Harabaz index
- Supervised and unsupervised learning of directed percolation
- Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transitions in the q-state clock models
- A Neural Networks study of the phase transitions of Potts model
- Transfer learning of phase transitions in percolation and directed percolation
- Neural Network flows of low q-state Potts and clock Models
- Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
- Machine learning for percolation utilizing auxiliary Ising variables
- On the generalizability of artificial neural networks in spin models
- Boltzmann-Machine Learning of Prior Distributions of Binarized Natural Images
- Calculation of the Critical Temperature for 2- and 3-Dimensional Ising Models and for 2-Dimensional Potts Models Using the Transfer Matrix Method