Encoder neural network in 2D acoustic tomography Full article
| Journal |
Applied and Computational Mathematics
ISSN: 1683-3511 |
||||||
|---|---|---|---|---|---|---|---|
| Output data | Year: 2024, Volume: 23, Number: 1, Pages: 83-98 Pages count : 16 DOI: 10.30546/1683-6154.23.1.2024.83 | ||||||
| Tags | Acoustic Tomography, Ultrasound, Deep Learning, Neural Networks, Coefficient Inverse Problem. | ||||||
| Authors |
|
||||||
| Affiliations |
|
Funding (1)
| 1 | Russian Science Foundation | 19-11-00154 |
Abstract:
We investigate deep learning approach in 2D dynamic ultrasound acoustic tomography. The mathematical model of acoustic tomography is described by a first-order hyperbolic system PDE and is based on conservation laws. This model guarantees us that the training sets of dynamic data are close to the physical solution. We train a neural network consisting of an encoder and a decoder with this data (they contain only one inclusion) and associate the data with a velocity coefficient. Numerical results show that we recover not only single inclusions, but also homogeneities consisting of two inclusions.
Cite:
Prikhodko A.Y.
, Shishlenin M.A.
, Novikov N.S.
, Klyuchinskiy D.V.
Encoder neural network in 2D acoustic tomography
Applied and Computational Mathematics. 2024. Т.23. №1. С.83-98. DOI: 10.30546/1683-6154.23.1.2024.83 WOS Scopus РИНЦ OpenAlex
Encoder neural network in 2D acoustic tomography
Applied and Computational Mathematics. 2024. Т.23. №1. С.83-98. DOI: 10.30546/1683-6154.23.1.2024.83 WOS Scopus РИНЦ OpenAlex
Dates:
| Published print: | Apr 15, 2024 |
| Published online: | Apr 15, 2024 |
Identifiers:
| Web of science: | WOS:001195457700003 |
| Scopus: | 2-s2.0-85187486568 |
| Elibrary: | 66215788 |
| OpenAlex: | W4392395418 |