Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks for Computational Optical Form Measurements   Deep neural networks have been successfully applied in many different fields
like computational imaging, medical healthcare, signal processing, or
autonomous driving. In a proof-of-principle study, we demonstrate that
computational optical form measurement can also benefit from deep learning. A
data-driven machine learning approach is explored to solve an inverse problem
in the accurate measurement of optical surfaces. The approach is developed and
tested using virtual measurements with known ground truth.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc