Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Observing how deep neural networks understand physics through the energy
  spectrum of one-dimensional quantum mechanics   We investigated how neural networks (NNs) understand physics using
one-dimensional quantum mechanics. After training an NN to accurately predict
energy eigenvalues from potentials, we used it to confirm the NN's
understanding of physics from four different aspects. The trained NN could
predict energy eigenvalues of a different potential than the one learned, focus
on minima and maxima of a potential, predict the probability distribution of
the existence of particles not used during training, and reproduce untrained
physical phenomena. These results show that NNs can learn the laws of physics
from only a limited set of data, predict the results of experiments under
conditions different from those used for training, and predict physical
quantities of types not provided during training. Since NNs understand physics
through a different path than humans take, and by complementing the human way
of understanding, they will be a powerful tool for advancing physics.

7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
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
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph