Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning hidden elasticity with deep neural networks   We introduce a de novo elastography method to learn the elasticity of solids
from measured strains. The deep neural network in our new method is supervised
by the theory of elasticity and does not require labeled data for training.
Results show that the proposed method can learn the hidden elasticity of solids
accurately and is robust when it comes to noisy and missing measurements. A
probable elasticity distribution for areas without measurements may also be
reconstructed by the neural network based on the elasticity distribution in
nearby regions. The neural network learns the hidden elasticity of solids as a
function of positions and thus it can generate elasticity images with an
arbitrary resolution. This feature is applied to create super-resolution
elasticity images in this study. We demonstrate that the neural network can
also learn the hidden physics when strain and elasticity distributions are both
given. The proposed method has various unique features and can be applied to a
broad range of elastography applications.

18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
1
The categories of this paper are: cs
2
The categories of this paper are: math