Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recent Trends in Artificial Intelligence-inspired Electronic Thermal
  Management   The rise of computation-based methods in thermal management has gained
immense attention in recent years due to the ability of deep learning to solve
complex 'physics' problems, which are otherwise difficult to be approached
using conventional techniques. Thermal management is required in electronic
systems to keep them from overheating and burning, enhancing their efficiency
and lifespan. For a long time, numerical techniques have been employed to aid
in the thermal management of electronics. However, they come with some
limitations. To increase the effectiveness of traditional numerical approaches
and address the drawbacks faced in conventional approaches, researchers have
looked at using artificial intelligence at various stages of the thermal
management process. The present study discusses in detail, the current uses of
deep learning in the domain of 'electronic' thermal management.

20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg