Learning the phase transitions of two-dimensional Potts model with a pre-trained one-dimensional neural network

Yuan Heng Tseng, Fu Jiun Jiang*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Conventionally, the training of a neural network for learning phases of matter uses real physical quantities as the training set. However, it has been demonstrated in several studies that this may not be required. Here we investigate the phase transitions of the two-dimensional (2D) q-state Potts models on the square lattice using a pre-trained neural network (NN). The employed NN was trained previously using two artificially made configurations as the training set. Hence no training is conducted for the present study. Remarkably, the used NN not only calculates the critical points of the considered phase transitions precisely, but also determines the nature of these phase transitions definitely without ambiguity. Our results as well as the outcomes found previously suggest that training a NN using real physical quantities as the training set is not required to obtain a working NN. These unconventional studies also imply that if the configuration space of a system can be classified into two categories, then the NN considered here is likely applicable to study the phase transition of that system. Comparison between our approach and other known NN methods for studying the 2D q-state Potts models is briefly discussed as well.

原文英語
文章編號107264
期刊Results in Physics
56
DOIs
出版狀態已發佈 - 2024 1月

ASJC Scopus subject areas

  • 一般物理與天文學

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