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Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm. / Penenko, Alexey; Mukatova, Zhadyra; Konopleva, Victoria.

2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 131-134 8880181 (2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Penenko, A, Mukatova, Z & Konopleva, V 2019, Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm. в 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019., 8880181, 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019, Institute of Electrical and Electronics Engineers Inc., стр. 131-134, 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019, Novosibirsk, Российская Федерация, 26.08.2019. https://doi.org/10.1109/OPCS.2019.8880181

APA

Penenko, A., Mukatova, Z., & Konopleva, V. (2019). Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm. в 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019 (стр. 131-134). [8880181] (2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OPCS.2019.8880181

Vancouver

Penenko A, Mukatova Z, Konopleva V. Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm. в 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. стр. 131-134. 8880181. (2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019). doi: 10.1109/OPCS.2019.8880181

Author

Penenko, Alexey ; Mukatova, Zhadyra ; Konopleva, Victoria. / Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm. 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 131-134 (2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019).

BibTeX

@inproceedings{dc21cd9fe1e340759f5c17aad2a76cdc,
title = "Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm",
abstract = "The problem of data assimilation for the advection diffusion model is considered. Data assimilation is carried out by choosing an uncertainty function that has the sense of the emission sources. Previously, a direct algorithm for data assimilation with a stabilizer in the cost functional governing the norm of the uncertainty function and its spatial derivative was introduced. In the paper, the assimilation parameters are found for a scenario with a known solution (training sample). The optimization is carried out by a genetic algorithm. The values found are used in scenarios with unknown emission sources (control experiment). The results of numerical experiments on solving a test problem are given.",
keywords = "advectiondiffusion model, data assimilation, genetic algorithm",
author = "Alexey Penenko and Zhadyra Mukatova and Victoria Konopleva",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019 ; Conference date: 26-08-2019 Through 30-08-2019",
year = "2019",
month = aug,
doi = "10.1109/OPCS.2019.8880181",
language = "English",
series = "2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "131--134",
booktitle = "2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019",
address = "United States",

}

RIS

TY - GEN

T1 - Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm

AU - Penenko, Alexey

AU - Mukatova, Zhadyra

AU - Konopleva, Victoria

N1 - Publisher Copyright: © 2019 IEEE.

PY - 2019/8

Y1 - 2019/8

N2 - The problem of data assimilation for the advection diffusion model is considered. Data assimilation is carried out by choosing an uncertainty function that has the sense of the emission sources. Previously, a direct algorithm for data assimilation with a stabilizer in the cost functional governing the norm of the uncertainty function and its spatial derivative was introduced. In the paper, the assimilation parameters are found for a scenario with a known solution (training sample). The optimization is carried out by a genetic algorithm. The values found are used in scenarios with unknown emission sources (control experiment). The results of numerical experiments on solving a test problem are given.

AB - The problem of data assimilation for the advection diffusion model is considered. Data assimilation is carried out by choosing an uncertainty function that has the sense of the emission sources. Previously, a direct algorithm for data assimilation with a stabilizer in the cost functional governing the norm of the uncertainty function and its spatial derivative was introduced. In the paper, the assimilation parameters are found for a scenario with a known solution (training sample). The optimization is carried out by a genetic algorithm. The values found are used in scenarios with unknown emission sources (control experiment). The results of numerical experiments on solving a test problem are given.

KW - advectiondiffusion model

KW - data assimilation

KW - genetic algorithm

UR - http://www.scopus.com/inward/record.url?scp=85078011691&partnerID=8YFLogxK

U2 - 10.1109/OPCS.2019.8880181

DO - 10.1109/OPCS.2019.8880181

M3 - Conference contribution

T3 - 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019

SP - 131

EP - 134

BT - 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019

Y2 - 26 August 2019 through 30 August 2019

ER -

ID: 23244680