Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning enabled fast evaluation of dynamic aperture for storage
  ring accelerators   For any storage ring-based large-scale scientific facility, one of the most
important performance parameters is the dynamic aperture (DA), which measures
the motion stability of charged particles in a global manner. To date,
long-term tracking-based simulation is regarded as the most reliable method to
calculate DA. However, numerical tracking may become a significant issue,
especially when lots of candidate designs of a storage ring need to be
evaluated. In this paper, we present a novel machine learning-based method,
which can reduce the computation cost of DA tracking by approximately one order
of magnitude, while keeping sufficiently high evaluation accuracy. Moreover, we
demonstrate that this method is independent of concrete physical models of a
storage ring. This method has the potential to be applied to similar problems
of identifying irregular motions in other complex dynamical systems.

28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
1
The categories of this paper are: cs
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex