Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient planning of peen-forming patterns via artificial neural
  networks   Robust automation of the shot peen forming process demands a closed-loop
feedback in which a suitable treatment pattern needs to be found in real-time
for each treatment iteration. In this work, we present a method for finding the
peen-forming patterns, based on a neural network (NN), which learns the
nonlinear function that relates a given target shape (input) to its optimal
peening pattern (output), from data generated by finite element simulations.
The trained NN yields patterns with an average binary accuracy of 98.8\% with
respect to the ground truth in microseconds.

33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat