Unsupervised learning of site percolation based on shuffled configurations

no code implementations • 20 Nov 2023 • Dian Xu, Shanshan Wang, Feng Gao, Wei Li, Jianmin Shen

In the field of statistical physics, machine learning has gained significant popularity and has achieved remarkable results in recent studies on phase transitions. In this paper, we apply Principal Component Analysis (PCA) and Autoencoder(AE) based on Unsupervised learning to study the various configurations of the percolation model in equilibrium phase transition.

Transfer learning of phase transitions in percolation and directed percolation

no code implementations • 31 Dec 2021 • Jianmin Shen, Feiyi Liu, Shiyang Chen, Dian Xu, Xiangna Chen, Shengfeng Deng, Wei Li, Gabor Papp, Chunbin Yang

With the DANN, only a small fraction of input configurations (2d images) needs to be labeled, which is automatically chosen, in order to capture the critical point.

Transfer Learning