Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovery of subdiffusion problem with noisy data via deep learning   Data-driven discovery of partial differential equations (PDEs) from observed
data in machine learning has been developed by embedding the discovery problem.
Recently, the discovery of traditional ODEs dynamics using linear multistep
methods in deep learning have been discussed in [Racheal and Du, SIAM J. Numer.
Anal. 59 (2021) 429-455; Du et al. arXiv:2103.11488]. We extend this framework
to the data-driven discovery of the time-fractional PDEs, which can effectively
characterize the ubiquitous power-law phenomena. In this paper, identifying
source function of subdiffusion with noisy data using L1 approximation in deep
neural network is presented. In particular, two types of networks for improving
the generalization of the subdiffusion problem are designed with noisy data.
The numerical experiments are given to illustrate the availability using deep
learning. To the best of our knowledge, this is the first topic on the
discovery of subdiffusion in deep learning with noisy data.

16
The categories of this paper are: stat
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg