Departamento de Sistemas de Computação - Universidade Federal da Paraíba. Brazil
Possui graduação em Matemática pela Universidade do Estado do Rio de Janeiro (1996), mestrado em Matemática pela Universidade Federal do Rio de Janeiro (2002), doutorado em Modelagem Computacional pelo Laboratório Nacional de Computação Científica (2007), Pós-Doutorado no Department of Applied Mathematics - University of Leeds, U.K, (2013/2014) e Pós-Doutorado no Instituto de Matemáticas de la Universidade de Sevilla, España (IMUS - 2017). Tem experiência na área de Matemática Computacional, atuando principalmente nos seguintes temas: Análise de Sensibilidade Topológica, Método das Soluções Fundamentais e Problemas Inversos. Foi agraciado com uma beca de Formación Permanente para movilidad del profesorado brasileño pela Fundación Carolina (2016). É elaborador de itens para o Enade/INEP.
A Genetic Algorithm for Pointwise Source Reconstruction by the Method of Fundamental Solutions
J. R. Faria
Abstract
Inverse source reconstruction problems offer great potential for applications of interest to engineering, such as the identification of polluting sources, and to medicine, such as electroencephalography, to cite at least two relevant examples. From a mathematical point of view, the identification of a concentrated source (intensity and location) corresponds to the identification of the centroid (location) and size (intensity) of a distributed source. On the other hand, from a numerical point of view, it is observed that the use of domain discretization methods is intrinsically associated with the introduction of numerical noise in reconstruction algorithms, which is strongly inadvisable since inverse problems are reckoned to be ill-posed. The objective of this work is to explore, in the context of a Poisson problem and taking into account a numerical point of view, a new reconstruction algorithm based on the method of fundamental solutions, where a source point adequately represents the pointwise source within the domain. The inverse problem is reformulated as an optimization problem solved through a genetic algorithm. Finally, numerical examples are performed to analyze the accuracy of the proposed algorithm for two and three dimensions.
Keywords
Inverse Problems, Method of Fundamental Solutions, Genetic Algorithms, Source Reconstruction