@Article{FurtadoCampMaca:2011:NeNeEm,
               author = "Furtado, Helaine Cristina Morais and de Campos Velho, Haroldo 
                         Fraga and Macau, Elbert Einstein Nehrer",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Neural networks for emulation variational method for data 
                         assimilation in nonlinear dynamics",
              journal = "Journal of Physics: Conference Series",
                 year = "2011",
               volume = "285",
               number = "012036",
                 note = "Setores de Atividade: Atividades profissionais, 
                         cient{\'{\i}}ficas e t{\'e}cnicas.",
             keywords = "Assimila{\c{c}}{\~a}o de Dados, Dinamica Nao-Linear, Controle 
                         Estocastico.",
             abstract = "Description 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.",
                  doi = "10.1088/1742-6596/285/1/012036",
                  url = "http://dx.doi.org/10.1088/1742-6596/285/1/012036",
                 issn = "1742-6588",
                label = "lattes: 0793627832164040 3 FurtadoCampMaca:2011:NeNeEm",
             language = "pt",
           targetfile = "1742-6596_285_1_012036.pdf",
        urlaccessdate = "29 jun. 2024"
}