解决莱恩-埃姆登方程的高效超几何小波方法

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-07-24 DOI:10.1016/j.jocs.2024.102392
B.J. Gireesha, K.J. Gowtham
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引用次数: 0

摘要

由于奇异点附近的系数发散,非线性初值/边界值问题的求解面临挑战。本研究介绍了一种基于超几何小波的新方法,旨在有效解决这些方程。专门的小波方法能有效处理奇异点,从而提高精度。为了评估这种方法的精确性和有效性,我们使用所提出的方法解决了 Lane-Emden 类型的问题,并与既定基准进行了比较。此外,还与其他小波方法进行了比较分析,包括绝对误差表和图形表示法。研究结果表明,与现有方法相比,拟议方法具有卓越的准确性和效率。这种方法的优点是需要的基函数较少,从而减少了计算时间和复杂性。
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Efficient hypergeometric wavelet approach for solving lane-emden equations

Nonlinear initial / boundary value problems present challenges in solving due to the divergence of coefficients near singular points. This study introduces a novel hypergeometric wavelet-based approach designed to effectively address these equations. The specialized wavelet method efficiently manages singularities, resulting in improved accuracy. To evaluate the precision and effectiveness of this approach, Lane-Emden type problems are solved using the proposed methodology and compared against established benchmarks. Comparative analyses with alternative wavelet methods are conducted, featuring absolute error tables and graphical representations. The findings highlight the exceptional accuracy and efficiency of the proposed method relative to existing approaches. An advantage of this method is its requirement of fewer basis functions, leading to reduced computational time and complexity.

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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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