Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning-based phase control method for coherent beam combining and
  its application in generating orbital angular momentum beams   We incorporate deep learning (DL) into coherent beam combining (CBC) systems
for the first time, to the best of our knowledge. Using a well-trained
convolutional neural network DL model, the phase error in CBC systems could be
accurately estimated and preliminarily compensated. Then, the residual phase
error is further compensated by stochastic parallel gradient descent (SPGD)
algorithms. The two-stage phase control strategy combined with DL and SPGD
algorithms is validated to be a feasible and promising technique to alleviate
the long-standing problem that the phase control bandwidth decreases as the
number of array elements expands. Further investigation denotes that the
proposed phase control technique could be employed to generate orbital angular
momentum (OAM) beams with different orders by distinguishing the OAM beams of
conjugated phase distributions.

17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph