Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extrapolating quantum observables with machine learning: Inferring
  multiple phase transitions from properties of a single phase   We present a machine-learning method for predicting sharp transitions in a
Hamiltonian phase diagram by extrapolating the properties of quantum systems.
The method is based on Gaussian Process regression with a combination of
kernels chosen through an iterative procedure maximizing the predicting power
of the kernels. The method is capable of extrapolating across the transition
lines. The calculations within a given phase can be used to predict not only
the closest sharp transition, but also a transition removed from the available
data by a separate phase. This makes the present method particularly valuable
for searching phase transitions in the parts of the parameter space that cannot
be probed experimentally or theoretically.

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The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio