Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Deep Learning to Extend the Range of Air-Pollution Monitoring and
  Forecasting   Across numerous applications, forecasting relies on numerical solvers for
partial differential equations (PDEs). Although the use of deep-learning
techniques has been proposed, actual applications have been restricted by the
fact the training data are obtained using traditional PDE solvers. Thereby, the
uses of deep-learning techniques were limited to domains, where the PDE solver
was applicable.
  We demonstrate a deep-learning framework for air-pollution monitoring and
forecasting that provides the ability to train across different model domains,
as well as a reduction in the run-time by two orders of magnitude. It presents
a first-of-a-kind implementation that combines deep-learning and
domain-decomposition techniques to allow model deployments extend beyond the
domain(s) on which the it has been trained.

29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys