@InCollection{CamposVelhoVijStePreNow:2002:NeNeIm,
               author = "Campos Velho, Haraldo Fraga and Vijaykumar, Nandamudi Lanlalapalli 
                         and Stephany, Stephan and Preto, Airam Jonatas and Nowosad, 
                         Alexandre Guirland",
                title = "A neural network implementation for data assimilation using MPI, 
                         application of high performace computing in engineering",
            booktitle = "Application of high performace computing in engineering",
            publisher = "WIT Press",
                 year = "2002",
               editor = "Brebia, C. A. and Melli, P. and Zanasi, A. .",
                pages = "Section 5, 211--220",
              address = "Southampton",
             keywords = "Neural networks, Data assimilation, COMPUTER SCIENCE.",
             abstract = "ABSTRACT: Data assimilation is a procedure that uses observational 
                         data to improve the prediction made by an inaccurate mathematical 
                         model, as is the case of numerical weather prediction, air quality 
                         problems and numerical oceanic simulation. In the case of 
                         atmospheric continuous data assimilation there are many 
                         deterministic and probabilistic methods. Deterministic methods 
                         include dynamic relaxation, variational methods and Laplace 
                         transform, whereas probabilistic methods include optimal 
                         interpolation and Kalman Filtering. Dynamic relaxation assumes the 
                         prediction model to be perfect, as does Laplace transform. 
                         Variational methods and optimal interpolation can be regarded as 
                         minimum-mean-square estimation of the atmosphere. In Kalman 
                         filtering the analysis innovation is computed as a linear function 
                         of the misfit between observation and forecast. The use of a 
                         Multilayer Perceptron Neural Network was proposed in order to 
                         emulate Kalman Filtering method aiming at the reduction of the 
                         processing time. The training phase of this neural network is 
                         controlled by a supervised learning algorithm. Adjustment of the 
                         network learning is conducted by a backpropagation algorithm. 
                         Classical, hardware-independent optimizations were performed in 
                         the sequential code and led to a significant reduction in the 
                         processing time for a given set of parameters. Fortran 90 language 
                         intrinsics eliminated inefficient hand-coded subroutines. A former 
                         attempt to parallelize the code and run it in a 4-processor shared 
                         memory machine, made use of HPF (High Performance Fortran) 
                         directives imbedded in the optimized code. This work presents an 
                         attempt to parallelize the related code through a message passing 
                         paradigm, particularly the MPI (Message Passing Interface) 
                         standard. Calls to the MPI communication library were imbedded in 
                         the optimized code in order to assign chunks of data to individual 
                         processors. Besides, the imbedding of HPF directives in the MPI 
                         version is expected to further improve the performance of the 
                         code..",
                label = "10669",
             language = "en",
          seriestitle = "Application of high performace computing in engineering",
           targetfile = "campos velho.pdf",
        urlaccessdate = "12 maio 2024"
}