Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Copula-based synthetic data augmentation for machine-learning emulators   Can we improve machine-learning (ML) emulators with synthetic data? If data
are scarce or expensive to source and a physical model is available,
statistically generated data may be useful for augmenting training sets
cheaply. Here we explore the use of copula-based models for generating
synthetically augmented datasets in weather and climate by testing the method
on a toy physical model of downwelling longwave radiation and corresponding
neural network emulator. Results show that for copula-augmented datasets,
predictions are improved by up to 62 % for the mean absolute error (from 1.17
to 0.44 W m$^{-2}$).

10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph