Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Symbolic integration by integrating learning models with different
  strengths and weaknesses   Integration is indispensable, not only in mathematics, but also in a wide
range of other fields. A deep learning method has recently been developed and
shown to be capable of integrating mathematical functions that could not
previously be integrated on a computer. However, that method treats integration
as equivalent to natural language translation and does not reflect mathematical
information. In this study, we adjusted the learning model to take mathematical
information into account and developed a wide range of learning models that
learn the order of numerical operations more robustly. In this way, we achieved
a 98.80% correct answer rate with symbolic integration, a higher rate than that
of any existing method. We judged the correctness of the integration based on
whether the derivative of the primitive function was consistent with the
integrand. By building an integrated model based on this strategy, we achieved
a 99.79% rate of correct answers with symbolic integration.

14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg