| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34R/43L8TKH | |||
| Repositório | sid.inpe.br/mtc-m21c/2020/11.23.10.37 | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21c/2020/11.23.10.37.22 | |||
| Última Atualização dos Metadados | 2022:01.04.01.35.38 (UTC) administrator | |||
| Chave Secundária | INPE--PRE/ | |||
| Chave de Citação | RosaACPBSSC:2020:SuNoPh | |||
| Título | Deep neural networks for learning spatiotemporal pattern formation: a survey in nonlinear physics  | |||
| Ano | 2020 | |||
| Data de Acesso | 13 maio 2024 | |||
| Tipo Secundário | PRE CN | |||
| 2. Contextualização | ||||
| Autor | 1 Rosa, Reinaldo Roberto 2 An, Wu Chun 3 Caproni, Anderson 4 Pontes, José 5 Barchi, Paulo Henrique 6 Stalder, Diego H. 7 Sautter, Rubens Andreas 8 Carvalho, Reinaldo Ramos de | |||
| Identificador de Curriculo | 1 8JMKD3MGP5W/3C9JJ5D 2 3 4 5 6 7 8 8JMKD3MGP5W/3C9JJ5B | |||
| Grupo | 1 LABAC-COCTE-INPE-MCTIC-GOV-BR 2 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR 3 4 5 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR 6 7 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 UNICSUL 4 Universidade do Estado do Rio de Janeiro (UERJ) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 7 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 reinaldo.rosa@inpe.br 2 3 4 5 paulo.barchi@inpe.br 6 7 rubens.sautter@inpe.br | |||
| Nome do Evento | Encontro de Outono Sociedade Brasileira de Física | |||
| Localização do Evento | Online | |||
| Data | 23 a 26 nov. | |||
| Histórico (UTC) | 2020-11-23 10:38:20 :: simone -> administrator :: 2020 2022-01-04 01:35:38 :: administrator -> simone :: 2020 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Resumo | Mining valuable knowledge from spatiotemporal data in nonlinear physics is critically important to many real world applications including reaction-diffusion, chaos and turbulence. As the complexity (volume, variety and resolution) of spatiotemporal data sets increases dramatically, traditional methods of data mining, especially methods based on supervised statistics, are becoming insufficient. With the recent advances in deep learning techniques (DLT), such as the recurrent neural network (RNN) and the convolutional neural network (CNN), considerable successes have been achieved in invariant machine learning tasks due to their powerful ability to learn hierarchical characteristics in spatial and temporal domains, and have been widely applied in various spatiotemporal data modeling tasks, such as pattern classification, predictive learning, representation learning and spatiotemporal anomaly detection. In this study, we provide a comprehensive survey on recent progress in applying deep learning techniques for spatiotemporal data mining (recognition, classification and prediction) from canonical nonlinear regimes in physics as reaction-diffusion from Ginzburg-Landau equation, spatiotemporal chaos from coupled map lattices and weak and fully developed turbulence from MHD. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to spatiotemporal pattern classification. We first categorize the types of spatiotemporal data combining accurate machine learning classifications from the CyMorph analysis with deep learning methodologies. Then a framework is introduced to show a general pipeline of the utilization of deep learning models. Next we investigated the power of generalization of DLT by operating small variations in the control parameters that are responsible for subtle changes in each group of simulated nonlinear processes including transitions from regular to irregular patterns and the appearance of remarkable structural aspects. Finally, we conclude the limitations of current research and point out future research directions. | |||
| Área | COMP | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Deep neural networks... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Deep neural networks... | |||
| Conteúdo da Pasta doc | não têm arquivos | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/3F2PHGS | |||
| Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notas | ||||
| Campos Vazios | archivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label language lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark tertiarytype type url versiontype volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||