Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continuous-mixture Autoregressive Networks for efficient variational
  calculation of many-body systems   We develop deep autoregressive networks with multi channels to compute
many-body systems with \emph{continuous} spin degrees of freedom directly. As a
concrete example, we embed the two-dimensional XY model into the
continuous-mixture networks and rediscover the Kosterlitz-Thouless (KT) phase
transition on a periodic square lattice. Vortices characterizing the quasi-long
range order are accurately detected by the autoregressive neural networks. By
learning the microscopic probability distributions from the macroscopic thermal
distribution, the neural networks compute the free energy directly and find
that free vortices and anti-vortices emerge in the high-temperature regime. As
a more precise evaluation, we compute the helicity modulus to determine the KT
transition temperature. Although the training process becomes more
time-consuming with larger lattice sizes, the training time remains unchanged
around the KT transition temperature. The continuous-mixture autoregressive
networks we developed thus can be potentially used to study other many-body
systems with continuous degrees of freedom.

35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an