Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning Calabi-Yau metrics   We apply machine learning to the problem of finding numerical Calabi-Yau
metrics. Building on Donaldson's algorithm for calculating balanced metrics on
K\"ahler manifolds, we combine conventional curve fitting and machine-learning
techniques to numerically approximate Ricci-flat metrics. We show that machine
learning is able to predict the Calabi-Yau metric and quantities associated
with it, such as its determinant, having seen only a small sample of training
data. Using this in conjunction with a straightforward curve fitting routine,
we demonstrate that it is possible to find highly accurate numerical metrics
much more quickly than by using Donaldson's algorithm alone, with our new
machine-learning algorithm decreasing the time required by between one and two
orders of magnitude.

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The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
1
The categories of this paper are: cs
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The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph