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

Alexey Penenko, Zhadyra Mukatova, Victoria Konopleva

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикации2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы131-134
Число страниц4
ISBN (электронное издание)9781728129860
DOI
СостояниеОпубликовано - авг 2019
Событие15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019 - Novosibirsk, Российская Федерация
Продолжительность: 26 авг 201930 авг 2019

Серия публикаций

Название2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019

Конференция

Конференция15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019
СтранаРоссийская Федерация
ГородNovosibirsk
Период26.08.201930.08.2019

Fingerprint Подробные сведения о темах исследования «Scenario Approach for the Optimization of Regularization Parameters in the Direct Variational Data Assimilation Algorithm». Вместе они формируют уникальный семантический отпечаток (fingerprint).

  • Цитировать

    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