REVOLUTIONIZING PDE SOLUTIONS: ANNEALING ALGORITHM AND POLYNOMIAL REGRESSION INTEGRATION
Published by: Qiang Zhang , Lingyun Li
Pages: 1-11
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This paper presents a novel approach for efficiently solving global solutions to partial differential equations (PDEs) using a combination of the Annealing Algorithm and Polynomial Regression tailored specifically for the Feynman-Kac formulation. By integrating the Annealing Algorithm with Polynomial Regression techniques, the proposed method offers enhanced accuracy and computational
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BEYOND TRADITIONAL APPROACHES: ENHANCING PDE SOLUTIONS WITH K-NEAREST NEIGHBOR APPROACH
Published by: Hui Wang , Lei Zhang
Pages: 12-23
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This paper introduces a novel approach, utilizing a modified method based on the K-nearest neighbor approach, for solving global solutions to partial differential equations (PDEs) through the Feynman-Kac formula. By integrating the K-nearest neighbor approach with the Feynman-Kac formula, this method offers enhanced accuracy and efficiency in obtaining global solutions to PDEs across various
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PREDICTIVE ANALYTICS IN DEMOGRAPHY: BIRTH POPULATION FORECASTING WITH GRAY VERHULST MODEL
Published by: Mingwei Liu , Yuxuan Wang
Pages: 24-31
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Forecasting birth population is integral to population studies, aiding policy formulation, development planning, resource allocation, economic growth, and social issue research. Accurate predictions serve as a foundation for sustainable development and population health enhancement. This paper addresses birth population prediction through mathematical modeling, a vital endeavor undertaken by
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