Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary
  Layer Using Artificial Neural Networks   We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.

34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas