A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations

We investigate the mathematical model of the 2D acoustic waves propagation in a heterogeneous domain. The hyperbolic first order system of partial differential equations is considered and solved by the Godunov method of the first order of approximation. This is a direct problem with appropriate init...

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Main Authors: Dmitriy Klyuchinskiy, Nikita Novikov, Maxim Shishlenin
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Computation
Subjects:
Online Access:https://www.mdpi.com/2079-3197/8/3/73
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author Dmitriy Klyuchinskiy
Nikita Novikov
Maxim Shishlenin
author_facet Dmitriy Klyuchinskiy
Nikita Novikov
Maxim Shishlenin
author_sort Dmitriy Klyuchinskiy
collection DOAJ
description We investigate the mathematical model of the 2D acoustic waves propagation in a heterogeneous domain. The hyperbolic first order system of partial differential equations is considered and solved by the Godunov method of the first order of approximation. This is a direct problem with appropriate initial and boundary conditions. We solve the coefficient inverse problem (IP) of recovering density. IP is reduced to an optimization problem, which is solved by the gradient descent method. The quality of the IP solution highly depends on the quantity of IP data and positions of receivers. We introduce a new approach for computing a gradient in the descent method in order to use as much IP data as possible on each iteration of descent.
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spelling doaj.art-d08eee5f2b8d439c9254504efeaa8dab2023-11-20T10:47:26ZengMDPI AGComputation2079-31972020-08-01837310.3390/computation8030073A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics EquationsDmitriy Klyuchinskiy0Nikita Novikov1Maxim Shishlenin2Department of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk 630090, RussiaDepartment of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk 630090, RussiaDepartment of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk 630090, RussiaWe investigate the mathematical model of the 2D acoustic waves propagation in a heterogeneous domain. The hyperbolic first order system of partial differential equations is considered and solved by the Godunov method of the first order of approximation. This is a direct problem with appropriate initial and boundary conditions. We solve the coefficient inverse problem (IP) of recovering density. IP is reduced to an optimization problem, which is solved by the gradient descent method. The quality of the IP solution highly depends on the quantity of IP data and positions of receivers. We introduce a new approach for computing a gradient in the descent method in order to use as much IP data as possible on each iteration of descent.https://www.mdpi.com/2079-3197/8/3/73acousticstomographyfirst-order hyperbolic systeminverse problemGodunov methodgradient descent method
spellingShingle Dmitriy Klyuchinskiy
Nikita Novikov
Maxim Shishlenin
A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
Computation
acoustics
tomography
first-order hyperbolic system
inverse problem
Godunov method
gradient descent method
title A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
title_full A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
title_fullStr A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
title_full_unstemmed A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
title_short A Modification of Gradient Descent Method for Solving Coefficient Inverse Problem for Acoustics Equations
title_sort modification of gradient descent method for solving coefficient inverse problem for acoustics equations
topic acoustics
tomography
first-order hyperbolic system
inverse problem
Godunov method
gradient descent method
url https://www.mdpi.com/2079-3197/8/3/73
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