Encoder neural network in 2D acoustic tomography | Sciact - Система учета научной деятельности ИМ СО РАН

Encoder neural network in 2D acoustic tomography Научная публикация

Журнал

Applied and Computational Mathematics
ISSN: 1683-3511

Вых. Данные

Год: 2024, Том: 23, Номер: 1, Страницы: 83-98 Страниц : 16 DOI: 10.30546/1683-6154.23.1.2024.83

Ключевые слова

Acoustic Tomography, Ultrasound, Deep Learning, Neural Networks, Coefficient Inverse Problem.

Авторы

Prikhodko A.Yu. ¹ , Shishlenin M.A. ^1,2,3 , Novikov N.S. ^1,2,3 , Klyuchinskiy D.V. ²

Организации

1	Novosibirsk State University
2	Institute of Computational Mathematics and Mathematical Geophysics
3	Sobolev Institute of Mathematics

Информация о финансировании (1)

Российский научный фонд

19-11-00154

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.

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

Опубликована в печати:	15 апр. 2024 г.
Опубликована online:	15 апр. 2024 г.

Web of science:	WOS:001195457700003
Scopus:	2-s2.0-85187486568
РИНЦ:	66215788
OpenAlex:	W4392395418

БД	Цитирований
Web of science	1
Scopus	3