Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mapping distinct phase transitions to a neural network   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 histogram reweighting to discover
one. We establish our approach in q-state Potts models and perform a
calculation for the critical coupling and the critical exponents of the
$\phi^{4}$ scalar field theory using quantities derived from the neural network
implementation. We view the machine learning algorithm as a mapping that
associates each configuration across different systems to its corresponding
phase and elaborate on implications for the discovery of unknown phase
transitions.

13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
19
The categories of this paper are: eess
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an