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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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Detalles Bibliográficos
Autores principales: Liu, Tao, Xue, Runqi, Liu, Chao, Qi, Yunfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625725/
https://www.ncbi.nlm.nih.gov/pubmed/34828178
http://dx.doi.org/10.3390/e23111480
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author Liu, Tao
Xue, Runqi
Liu, Chao
Qi, Yunfei
author_facet Liu, Tao
Xue, Runqi
Liu, Chao
Qi, Yunfei
author_sort Liu, Tao
collection PubMed
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.
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spelling pubmed-86257252021-11-27 A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations Liu, Tao Xue, Runqi Liu, Chao Qi, Yunfei Entropy (Basel) Article 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. MDPI 2021-11-09 /pmc/articles/PMC8625725/ /pubmed/34828178 http://dx.doi.org/10.3390/e23111480 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Tao
Xue, Runqi
Liu, Chao
Qi, Yunfei
A Regularization Homotopy Strategy for the Constrained Parameter Inversion of Partial Differential Equations
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625725/
https://www.ncbi.nlm.nih.gov/pubmed/34828178
http://dx.doi.org/10.3390/e23111480
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