Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning physical descriptors for materials science by compressed
  sensing   The availability of big data in materials science offers new routes for
analyzing materials properties and functions and achieving scientific
understanding. Finding structure in these data that is not directly visible by
standard tools and exploitation of the scientific information requires new and
dedicated methodology based on approaches from statistical learning, compressed
sensing, and other recent methods from applied mathematics, computer science,
statistics, signal processing, and information science. In this paper, we
explain and demonstrate a compressed-sensing based methodology for feature
selection, specifically for discovering physical descriptors, i.e., physical
parameters that describe the material and its properties of interest, and
associated equations that explicitly and quantitatively describe those relevant
properties. As showcase application and proof of concept, we describe how to
build a physical model for the quantitative prediction of the crystal structure
of binary compound semiconductors.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin