Standard

CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach. / Klyuchinskiy, Dmitriy V.; Novikov, Nikita S.; Shishlenin, Maxim A.

In: Journal of Computational Physics, Vol. 439, 110374, 15.08.2021.

Research output: Contribution to journal › Article › peer-review

Harvard

Klyuchinskiy, DV, Novikov, NS & Shishlenin, MA 2021, 'CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach', Journal of Computational Physics, vol. 439, 110374. https://doi.org/10.1016/j.jcp.2021.110374

APA

Klyuchinskiy, D. V., Novikov, N. S., & Shishlenin, M. A. (2021). CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach. Journal of Computational Physics, 439, [110374]. https://doi.org/10.1016/j.jcp.2021.110374

Vancouver

Klyuchinskiy DV, Novikov NS, Shishlenin MA. CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach. Journal of Computational Physics. 2021 Aug 15;439:110374. doi: 10.1016/j.jcp.2021.110374

Author

Klyuchinskiy, Dmitriy V. ; Novikov, Nikita S. ; Shishlenin, Maxim A. / CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach. In: Journal of Computational Physics. 2021 ; Vol. 439.

BibTeX

@article{c3563a7f190247d18cb136e42595a528,
title = "CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach",
abstract = "Numerical solution of inverse problem for 2D acoustic system of conservation laws by gradient type method requires storage of O(N3) elements which is crucial on large grids with O(N) points in single dimension. In this article we present an approach to save twice memory on the stage of adjoint problem and gradient calculation and compare it with usual approach in memory and CPU time cost. Numerical comparison for CPU time and memory of one step of iteration process which consists of direct problem solution, adjoint problem solution and calculation of the gradient are presented.",
keywords = "Acoustic, Adjoint problem, Coefficient inverse problem, Conservation laws, Gradient, RAM optimization",
author = "Klyuchinskiy, {Dmitriy V.} and Novikov, {Nikita S.} and Shishlenin, {Maxim A.}",
note = "Funding Information: This work is the results of the research project funded by Russian Science Foundation under grant 19-11-00154 “Developing of new mathematical models of acoustic tomography in medicine. Numerical methods, HPC and software”. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = aug,
day = "15",
doi = "10.1016/j.jcp.2021.110374",
language = "English",
volume = "439",
journal = "Journal of Computational Physics",
issn = "0021-9991",
publisher = "Academic Press Inc.",

}

RIS

TY - JOUR

T1 - CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach

AU - Klyuchinskiy, Dmitriy V.

AU - Novikov, Nikita S.

AU - Shishlenin, Maxim A.

N1 - Funding Information: This work is the results of the research project funded by Russian Science Foundation under grant 19-11-00154 “Developing of new mathematical models of acoustic tomography in medicine. Numerical methods, HPC and software”. Publisher Copyright: © 2021 Elsevier Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/8/15

Y1 - 2021/8/15

N2 - Numerical solution of inverse problem for 2D acoustic system of conservation laws by gradient type method requires storage of O(N3) elements which is crucial on large grids with O(N) points in single dimension. In this article we present an approach to save twice memory on the stage of adjoint problem and gradient calculation and compare it with usual approach in memory and CPU time cost. Numerical comparison for CPU time and memory of one step of iteration process which consists of direct problem solution, adjoint problem solution and calculation of the gradient are presented.

AB - Numerical solution of inverse problem for 2D acoustic system of conservation laws by gradient type method requires storage of O(N3) elements which is crucial on large grids with O(N) points in single dimension. In this article we present an approach to save twice memory on the stage of adjoint problem and gradient calculation and compare it with usual approach in memory and CPU time cost. Numerical comparison for CPU time and memory of one step of iteration process which consists of direct problem solution, adjoint problem solution and calculation of the gradient are presented.

KW - Acoustic

KW - Adjoint problem

KW - Coefficient inverse problem

KW - Conservation laws

KW - Gradient

KW - RAM optimization

UR - http://www.scopus.com/inward/record.url?scp=85105593133&partnerID=8YFLogxK

U2 - 10.1016/j.jcp.2021.110374

DO - 10.1016/j.jcp.2021.110374

M3 - Article

AN - SCOPUS:85105593133

VL - 439

JO - Journal of Computational Physics

JF - Journal of Computational Physics

SN - 0021-9991

M1 - 110374

ER -

ID: 28553858