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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/39RQE75
Repositorydpi.inpe.br/plutao/2011/06.11.03.31.33   (restricted access)
Last Update2011:10.18.13.01.29 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao/2011/06.11.03.31.34
Metadata Last Update2018:06.05.00.01.19 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1088/1742-6596/285/1/012036
ISSN1742-6588
Labellattes: 0793627832164040 3 FurtadoCampMaca:2011:NeNeEm
Citation KeyFurtadoCampMaca:2011:NeNeEm
TitleNeural networks for emulation variational method for data assimilation in nonlinear dynamics
Year2011
Access Date2024, June 13
Secondary TypePRE PI
Number of Files1
Size1294 KiB
2. Context
Author1 Furtado, Helaine Cristina Morais
2 de Campos Velho, Haroldo Fraga
3 Macau, Elbert Einstein Nehrer
Resume Identifier1
2 8JMKD3MGP5W/3C9JHC3
3 8JMKD3MGP5W/3C9JGUT
Group1
2 LAC-CTE-INPE-MCT-BR
3 LAC-CTE-INPE-MCT-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3 elbert@lac.inpe.br
e-Mail Addresselbert@lac.inpe.br
JournalJournal of Physics: Conference Series
Volume285
Number012036
Secondary MarkC_ASTRONOMIA_/_FÍSICA B4_CIÊNCIA_DA_COMPUTAÇÃO C_CIÊNCIAS_BIOLÓGICAS_II C_ENGENHARIAS_I B3_ENGENHARIAS_II C_ENGENHARIAS_III B1_INTERDISCIPLINAR B3_MATERIAIS C_QUÍMICA
History (UTC)2011-06-11 17:43:45 :: lattes -> administrator :: 2011
2011-07-19 22:14:47 :: administrator -> marciana :: 2011
2011-10-18 13:01:29 :: marciana -> administrator :: 2011
2018-06-05 00:01:19 :: administrator -> marciana :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsAssimilação de Dados
Dinamica Nao-Linear
Controle Estocastico
AbstractDescription of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to improve the prediction is to add some information from the real dynamics into mathematical model. This aditional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Variational method in context of data assimilation. Techniques for data assimilation are applied for the Lorenz systems; which presents a strong nonlinearity and chaotic nature.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Neural networks for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languagept
Target File1742-6596_285_1_012036.pdf
User Groupadministrator
lattes
marciana
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesSetores de Atividade: Atividades profissionais, científicas e técnicas.
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository month nextedition orcid pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url versiontype
7. Description control
e-Mail (login)marciana
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