Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Physics through Images: An Application to Wind-Driven Spatial
  Patterns   For centuries, scientists have observed nature to understand the laws that
govern the physical world. The traditional process of turning observations into
physical understanding is slow. Imperfect models are constructed and tested to
explain relationships in data. Powerful new algorithms are available that can
enable computers to learn physics by observing images and videos. Inspired by
this idea, instead of training machine learning models using physical
quantities, we trained them using images, that is, pixel information. For this
work, and as a proof of concept, the physics of interest are wind-driven
spatial patterns. Examples of these phenomena include features in Aeolian dunes
and the deposition of volcanic ash, wildfire smoke, and air pollution plumes.
We assume that the spatial patterns were collected by an imaging device that
records the magnitude of the logarithm of deposition as a red, green, blue
(RGB) color image with channels containing values ranging from 0 to 255. In
this paper, we explore deep convolutional neural network-based autoencoders to
exploit relationships in wind-driven spatial patterns, which commonly occur in
geosciences, and reduce their dimensionality. Reducing the data dimension size
with an encoder allows us to train regression models linking geographic and
meteorological scalar input quantities to the encoded space. Once this is
achieved, full predictive spatial patterns are reconstructed using the decoder.
We demonstrate this approach on images of spatial deposition from a pollution
source, where the encoder compresses the dimensionality to 0.02% of the
original size and the full predictive model performance on test data achieves
an accuracy of 92%.

30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys