@InProceedings{VijaykumarStePreCamNow:2002:OpNeNe,
               author = "Vijaykumar, Nandamudi Lankalapali and Stephanyl, Stephan and 
                         Preto, Airam Jonatas and Campos Velho, Haroldo Fraga de and 
                         Nowosad, Alexandre G.",
          affiliation = "{INPE-Sao Jose dos Campos-12227-010-SP-Brasil}",
                title = "Optimized Neural Network Code for Data Assimilation",
            booktitle = "Anais...",
                 year = "2002",
                pages = "3841--3849",
         organization = "Congresso Brasileiro de Meteorologia, 12.",
             abstract = "The data assimilation process can be described as a procedure that 
                         uses observational data to improve the prediction made by an 
                         inaccurate mathematical modelo Recent1y, neural networks have been 
                         proposed as a new method for data assimilation. The Multilayer 
                         Perceptron network with backpropagation learning was chosen for 
                         this procedure. Neural networks are inherent1y a parallel 
                         procedure. This paper presents some strategies being used to 
                         achieve an optimized parallel code for the network training. Code 
                         optimizations include the use of either High Perfonnance Fortran 
                         directives or Message Passing Interface library calls. A neural 
                         network for Data Assimilation was trained based on both the 
                         physical models of the Lorenz and shallow water equations.",
  conference-location = "Foz do Iguacu",
      conference-year = "4-9 ago.",
             language = "en",
         organisation = "SBMET",
        urlaccessdate = "29 jun. 2024"
}