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Papers by Helaine Cristina Morais Furtado

Research paper thumbnail of q-Calculus Formalism for Non-extensive Particle Filter

Integral Methods in Science and Engineering, 2019

A class of sequential Monte Carlo estimation is frequently called particle filter. This filter be... more A class of sequential Monte Carlo estimation is frequently called particle filter. This filter belongs to the Bayesian strategy for estimation, where a non-linear and non-Gaussian assumptions can be applied. Here, the Tsallis’ distribution, from the non-extensive thermo-statistics, is used to design the best likelihood operator. Therefore, no previous likelihood operator is assumed. The new filter formulation will be named as non-extensive particle filter (NEx-PF). The distribution estimated by the NEx-PF can compute the standard form of the central limit theorem, as well as the Levy-Gnedenko central limit theorem. The q-calculus formalism is employed to generalize some definitions and properties.

Research paper thumbnail of Artificial neural networking to data assimilation at ocean circulation model

Research paper thumbnail of Neural network for performance improvement in atmospheric prediction systems: Data Assimilation

Anais do 11. Congresso Brasileiro de Inteligência Computacional, 2016

Research paper thumbnail of Artificial Neural Networks emulating Representer Method at a shallow water model 2D

Research paper thumbnail of Redes neurais e diferentes métodos de assimilação de dados em dinâmica não linear

Research paper thumbnail of Grid Computing for Climate Processing on South America

Research paper thumbnail of Automatic configuration for neural network with application to the data assimilation

Research paper thumbnail of Artificial Neural Networks Applied to the Lorenz Dynamical System via Variational Data Assimilation

Technique for data assimilation is a crucial issue for the prediction operational process based o... more Technique for data assimilation is a crucial issue for the prediction operational process based on a nice combination between data from a mathematical model and observational data. Kalnay (2003) has pointed out that the numerical weather prediction (NWP) is an initial-value problem: given an estimate of the present state of the atmosphere, the model simulates (forecasts) its evolution. The problem of determination of the initial conditions for a prediction model is very important and complex, and it has became into a science itself (Daley, 1991). However, NWP models are obviously not perfect models of the atmosphere, so the use these models to perform predictions is liable to the error. However, there is an observation system, the use this information must be added in order to have a better prediction. Therefore, data assimilation is a method by which the initial conditions for the model are determined from combining of observation data and data from mathematical models. Obviously, ...

Research paper thumbnail of Data assimilation by variational method in differential equations

Research paper thumbnail of Data Assimilation by Neural Network on Hardware Device

Data assimilation combines observation and data from a mathematical model to compute the best ini... more Data assimilation combines observation and data from a mathematical model to compute the best initial condition. Inverse problems can be classified according to the nature of the estimated property type: source term, system property, boundary condition, and initial condition. Extended Kalman filter (EKF) and four dimensional variational method (4D-Var) are two procedures employed to perform DA. Artificial neural networks are applied here for reducing the computational complexity for a given DA scheme. The supervised multi-layer perceptron is used to emulate the Kalman filter. The designed neural network was implemented on a FPGA (Field-programmable Gate Array), employed as a co-processor. The linear 1D wave equation is the dynamical system used for testing the framework. A good performance was obtained with neural network emulating the Kalman filter, with an average error 5.18E-05 (software and FPGA comparison) on the FPGA implementation. Introduction Inverse problems can be classif...

Research paper thumbnail of Neural network for data assimilation method applied to shallow water equation

ABSTRACT The description of the most physical phenomena is based on differential equations. But, ... more ABSTRACT The description of the most physical phenomena is based on differential equations. But, the modeling error is a permanent feature. One basic uncertainty is to identify the initial condition. For the operational prediction systems, a strategy to deal with such uncertainty is to add some information from the real world into the mathematical model. This additional information consists of observations (measurement values). However, the observed data might be carefully inserted, in order to avoid negative impacto on the prediction. Techniques for data assimilation are tools to produce an effective combination of two sources of data: observation and model, for computing the analysis. The analysis is the initial condition used in the prediction computer model. The goal of the present work is to employ artificialneural networks as a data assimilation method applied to shallow water equation used to represent ocean dynamics.

Research paper thumbnail of Assimilação De Dados Com Redes Neurais Artificiais Em Equações Diferenciais

Research paper thumbnail of Assimilaç ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıculas

Page 1. Assimilaç ˜ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıcu... more Page 1. Assimilaç ˜ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıculas Helaine CM Furtado1, Haroldo F. de Campo Velho2, Elbert EN Macau2, Fabrıcio Härter 3 1Programa de pós-graduaç˜ao em Computaç˜ao Aplicada (CAP) ...

Research paper thumbnail of Neural networks for emulation variational method for data assimilation in nonlinear dynamics

Journal of Physics: Conference Series, 2011

Research paper thumbnail of Data assimilation: Particle filter and artificial neural networks

Journal of Physics: Conference Series, 2008

... Helaine Cristina Morais Furtado, Haroldo Fraga de Campos Velho, Elbert Einstein Nehrer Macau ... more ... Helaine Cristina Morais Furtado, Haroldo Fraga de Campos Velho, Elbert Einstein Nehrer Macau Laboratório Associado de Computaç˜ao e Matemática Aplicada (LAC), Instituto Nacional de Pesquisas Espaciais (INPE), PO Box 515, 12245-970, S˜ao José dos Campos, SP ...

Research paper thumbnail of q-Calculus Formalism for Non-extensive Particle Filter

Integral Methods in Science and Engineering, 2019

A class of sequential Monte Carlo estimation is frequently called particle filter. This filter be... more A class of sequential Monte Carlo estimation is frequently called particle filter. This filter belongs to the Bayesian strategy for estimation, where a non-linear and non-Gaussian assumptions can be applied. Here, the Tsallis’ distribution, from the non-extensive thermo-statistics, is used to design the best likelihood operator. Therefore, no previous likelihood operator is assumed. The new filter formulation will be named as non-extensive particle filter (NEx-PF). The distribution estimated by the NEx-PF can compute the standard form of the central limit theorem, as well as the Levy-Gnedenko central limit theorem. The q-calculus formalism is employed to generalize some definitions and properties.

Research paper thumbnail of Artificial neural networking to data assimilation at ocean circulation model

Research paper thumbnail of Neural network for performance improvement in atmospheric prediction systems: Data Assimilation

Anais do 11. Congresso Brasileiro de Inteligência Computacional, 2016

Research paper thumbnail of Artificial Neural Networks emulating Representer Method at a shallow water model 2D

Research paper thumbnail of Redes neurais e diferentes métodos de assimilação de dados em dinâmica não linear

Research paper thumbnail of Grid Computing for Climate Processing on South America

Research paper thumbnail of Automatic configuration for neural network with application to the data assimilation

Research paper thumbnail of Artificial Neural Networks Applied to the Lorenz Dynamical System via Variational Data Assimilation

Technique for data assimilation is a crucial issue for the prediction operational process based o... more Technique for data assimilation is a crucial issue for the prediction operational process based on a nice combination between data from a mathematical model and observational data. Kalnay (2003) has pointed out that the numerical weather prediction (NWP) is an initial-value problem: given an estimate of the present state of the atmosphere, the model simulates (forecasts) its evolution. The problem of determination of the initial conditions for a prediction model is very important and complex, and it has became into a science itself (Daley, 1991). However, NWP models are obviously not perfect models of the atmosphere, so the use these models to perform predictions is liable to the error. However, there is an observation system, the use this information must be added in order to have a better prediction. Therefore, data assimilation is a method by which the initial conditions for the model are determined from combining of observation data and data from mathematical models. Obviously, ...

Research paper thumbnail of Data assimilation by variational method in differential equations

Research paper thumbnail of Data Assimilation by Neural Network on Hardware Device

Data assimilation combines observation and data from a mathematical model to compute the best ini... more Data assimilation combines observation and data from a mathematical model to compute the best initial condition. Inverse problems can be classified according to the nature of the estimated property type: source term, system property, boundary condition, and initial condition. Extended Kalman filter (EKF) and four dimensional variational method (4D-Var) are two procedures employed to perform DA. Artificial neural networks are applied here for reducing the computational complexity for a given DA scheme. The supervised multi-layer perceptron is used to emulate the Kalman filter. The designed neural network was implemented on a FPGA (Field-programmable Gate Array), employed as a co-processor. The linear 1D wave equation is the dynamical system used for testing the framework. A good performance was obtained with neural network emulating the Kalman filter, with an average error 5.18E-05 (software and FPGA comparison) on the FPGA implementation. Introduction Inverse problems can be classif...

Research paper thumbnail of Neural network for data assimilation method applied to shallow water equation

ABSTRACT The description of the most physical phenomena is based on differential equations. But, ... more ABSTRACT The description of the most physical phenomena is based on differential equations. But, the modeling error is a permanent feature. One basic uncertainty is to identify the initial condition. For the operational prediction systems, a strategy to deal with such uncertainty is to add some information from the real world into the mathematical model. This additional information consists of observations (measurement values). However, the observed data might be carefully inserted, in order to avoid negative impacto on the prediction. Techniques for data assimilation are tools to produce an effective combination of two sources of data: observation and model, for computing the analysis. The analysis is the initial condition used in the prediction computer model. The goal of the present work is to employ artificialneural networks as a data assimilation method applied to shallow water equation used to represent ocean dynamics.

Research paper thumbnail of Assimilação De Dados Com Redes Neurais Artificiais Em Equações Diferenciais

Research paper thumbnail of Assimilaç ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıculas

Page 1. Assimilaç ˜ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıcu... more Page 1. Assimilaç ˜ao de Dados com Redes Neurais emulando o Filtro de Kalman eo Filtro de Partıculas Helaine CM Furtado1, Haroldo F. de Campo Velho2, Elbert EN Macau2, Fabrıcio Härter 3 1Programa de pós-graduaç˜ao em Computaç˜ao Aplicada (CAP) ...

Research paper thumbnail of Neural networks for emulation variational method for data assimilation in nonlinear dynamics

Journal of Physics: Conference Series, 2011

Research paper thumbnail of Data assimilation: Particle filter and artificial neural networks

Journal of Physics: Conference Series, 2008

... Helaine Cristina Morais Furtado, Haroldo Fraga de Campos Velho, Elbert Einstein Nehrer Macau ... more ... Helaine Cristina Morais Furtado, Haroldo Fraga de Campos Velho, Elbert Einstein Nehrer Macau Laboratório Associado de Computaç˜ao e Matemática Aplicada (LAC), Instituto Nacional de Pesquisas Espaciais (INPE), PO Box 515, 12245-970, S˜ao José dos Campos, SP ...