Mapping distinct phase transitions to a neural network.

@article{Bachtis2020MappingDP,
  title={Mapping distinct phase transitions to a neural network.},
  author={Dimitrios Bachtis and Gert Aarts and Biagio Lucini},
  journal={Physical review. E},
  year={2020},
  volume={102 5-1},
  pages={
          053306
        }
}
We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symmetry-breaking phase transitions in considered systems irrespective of the universality class, order, and the presence of discrete or continuous degrees of freedom. No prior knowledge about the existence of a phase transition is required in the target system and its entire parameter space can be scanned with multiple… 

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