A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model
Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient desce...
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Online Access: | https://doi.org/10.5194/os-9-609-2013 http://www.ocean-sci.net/9/609/2013/os-9-609-2013.pdf https://doaj.org/article/d41b27d2434b4147826d52b1e3e786b1 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:d41b27d2434b4147826d52b1e3e786b1 2023-05-15T15:07:27+02:00 A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model H. Sumata F. Kauker R. Gerdes C. Köberle M. Karcher 2013-07-01 https://doi.org/10.5194/os-9-609-2013 http://www.ocean-sci.net/9/609/2013/os-9-609-2013.pdf https://doaj.org/article/d41b27d2434b4147826d52b1e3e786b1 en eng Copernicus Publications doi:10.5194/os-9-609-2013 1812-0784 1812-0792 http://www.ocean-sci.net/9/609/2013/os-9-609-2013.pdf https://doaj.org/article/d41b27d2434b4147826d52b1e3e786b1 undefined Ocean Science, Vol 9, Iss 4, Pp 609-630 (2013) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2013 fttriple https://doi.org/10.5194/os-9-609-2013 2023-01-22T17:51:31Z Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model–data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations were conducted that differ in the model formulation ("smoothed code" versus standard code) with respect to the FD method and in the population size and number of possibilities with respect to the μGA method. The FD method fails to estimate optimal parameters due to the ill-shaped nature of the cost function caused by the strong non-linearity of the system, whereas the genetic algorithms can effectively estimate near optimal parameters. The results of the study indicate that the sophisticated stochastic approach (μGA) is of practical use for parameter optimization of a coupled ocean–sea ice model with a medium-sized horizontal resolution of 50 km × 50 km as used in this study. Article in Journal/Newspaper Arctic Sea ice Unknown Arctic Ocean Science 9 4 609 630 |
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geo envir |
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geo envir H. Sumata F. Kauker R. Gerdes C. Köberle M. Karcher A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
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geo envir |
description |
Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model–data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations were conducted that differ in the model formulation ("smoothed code" versus standard code) with respect to the FD method and in the population size and number of possibilities with respect to the μGA method. The FD method fails to estimate optimal parameters due to the ill-shaped nature of the cost function caused by the strong non-linearity of the system, whereas the genetic algorithms can effectively estimate near optimal parameters. The results of the study indicate that the sophisticated stochastic approach (μGA) is of practical use for parameter optimization of a coupled ocean–sea ice model with a medium-sized horizontal resolution of 50 km × 50 km as used in this study. |
format |
Article in Journal/Newspaper |
author |
H. Sumata F. Kauker R. Gerdes C. Köberle M. Karcher |
author_facet |
H. Sumata F. Kauker R. Gerdes C. Köberle M. Karcher |
author_sort |
H. Sumata |
title |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
title_short |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
title_full |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
title_fullStr |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
title_full_unstemmed |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
title_sort |
comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
publisher |
Copernicus Publications |
publishDate |
2013 |
url |
https://doi.org/10.5194/os-9-609-2013 http://www.ocean-sci.net/9/609/2013/os-9-609-2013.pdf https://doaj.org/article/d41b27d2434b4147826d52b1e3e786b1 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Ocean Science, Vol 9, Iss 4, Pp 609-630 (2013) |
op_relation |
doi:10.5194/os-9-609-2013 1812-0784 1812-0792 http://www.ocean-sci.net/9/609/2013/os-9-609-2013.pdf https://doaj.org/article/d41b27d2434b4147826d52b1e3e786b1 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/os-9-609-2013 |
container_title |
Ocean Science |
container_volume |
9 |
container_issue |
4 |
container_start_page |
609 |
op_container_end_page |
630 |
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