Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning in a data-limited regime: Augmenting experiments with
  synthetic data uncovers order in crumpled sheets   Machine learning has gained widespread attention as a powerful tool to
identify structure in complex, high-dimensional data. However, these techniques
are ostensibly inapplicable for experimental systems where data is scarce or
expensive to obtain. Here we introduce a strategy to resolve this impasse by
augmenting the experimental dataset with synthetically generated data of a much
simpler sister system. Specifically, we study spontaneously emerging local
order in crease networks of crumpled thin sheets, a paradigmatic example of
spatial complexity, and show that machine learning techniques can be effective
even in a data-limited regime. This is achieved by augmenting the scarce
experimental dataset with inexhaustible amounts of simulated data of rigid
flat-folded sheets, which are simple to simulate and share common statistical
properties. This significantly improves the predictive power in a test problem
of pattern completion and demonstrates the usefulness of machine learning in
bench-top experiments where data is good but scarce.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys