A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations

The main difficulty posed by the parameter inversion of partial differential equations lies in the presence of numerous local minima in the cost function. Inversion fails to converge to the global minimum point unless the initial estimate is close to the exact solution. Constraints can improve the c...

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Autores principales: Tao Liu, Runqi Xue, Chao Liu, Yunfei Qi
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Publicado: MDPI AG 2021
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spelling oai:doaj.org-article:ccdd0e50fc6f4c899e88f50f54864ee32021-11-25T17:29:59ZA Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations10.3390/e231114801099-4300https://doaj.org/article/ccdd0e50fc6f4c899e88f50f54864ee32021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1480https://doaj.org/toc/1099-4300The main difficulty posed by the parameter inversion of partial differential equations lies in the presence of numerous local minima in the cost function. Inversion fails to converge to the global minimum point unless the initial estimate is close to the exact solution. Constraints can improve the convergence of the method, but ordinary iterative methods will still become trapped in local minima if the initial guess is far away from the exact solution. In order to overcome this drawback fully, this paper designs a homotopy strategy that makes natural use of constraints. Furthermore, due to the ill-posedness of inverse problem, the standard Tikhonov regularization is incorporated. The efficiency of the method is illustrated by solving the coefficient inversion of the saturation equation in the two-phase porous media.Tao LiuRunqi XueChao LiuYunfei QiMDPI AGarticleparameter inversionpartial differential equationTikhonov regularizationhomotopy methodconstraintsScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1480, p 1480 (2021)
institution DOAJ
collection DOAJ
language EN
topic parameter inversion
partial differential equation
Tikhonov regularization
homotopy method
constraints
Science
Q
Astrophysics
QB460-466
Physics
QC1-999
spellingShingle parameter inversion
partial differential equation
Tikhonov regularization
homotopy method
constraints
Science
Q
Astrophysics
QB460-466
Physics
QC1-999
Tao Liu
Runqi Xue
Chao Liu
Yunfei Qi
A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
description The main difficulty posed by the parameter inversion of partial differential equations lies in the presence of numerous local minima in the cost function. Inversion fails to converge to the global minimum point unless the initial estimate is close to the exact solution. Constraints can improve the convergence of the method, but ordinary iterative methods will still become trapped in local minima if the initial guess is far away from the exact solution. In order to overcome this drawback fully, this paper designs a homotopy strategy that makes natural use of constraints. Furthermore, due to the ill-posedness of inverse problem, the standard Tikhonov regularization is incorporated. The efficiency of the method is illustrated by solving the coefficient inversion of the saturation equation in the two-phase porous media.
format article
author Tao Liu
Runqi Xue
Chao Liu
Yunfei Qi
author_facet Tao Liu
Runqi Xue
Chao Liu
Yunfei Qi
author_sort Tao Liu
title A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title_short A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title_full A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title_fullStr A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title_full_unstemmed A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title_sort regularization homotopy strategy for the constrained parameter inversion of partial differential equations
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/ccdd0e50fc6f4c899e88f50f54864ee3
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