{"title":"An efficient 6th-order compact difference scheme with error estimation for nonlocal Lane–Emden equation","authors":"Nirupam Sahoo, Randhir Singh","doi":"10.1016/j.jocs.2025.102529","DOIUrl":null,"url":null,"abstract":"<div><div>In this manuscript, we present a new and efficient 6th-order compact difference method for solving the nonlocal Lane–Emden equation. This method effectively addresses singular-type problems without the need to modify or remove the singularities. To achieve this, we construct a uniform mesh across the domain and develop a new sixth-order discrete method that approximates the derivatives, transforming the differential equation into a system of equations. Use Newton or any iterative technique to obtain the numerical solution of the system of equations. Our new scheme efficiently handles the singularity at <span><math><mrow><mi>t</mi><mo>=</mo><mn>0</mn></mrow></math></span>. Additionally, we conduct a mathematical analysis of the method’s consistency, stability, error bounds, and convergence rate. We also include several numerical problems from the existing literature to demonstrate the accuracy, efficiency, and applicability of the proposed scheme. Also, compare the numerical approximations with the existing recent techniques. The newly proposed scheme offers 6th-order accuracy using a small-size matrix and delivers better numerical results than the existing methods.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102529"},"PeriodicalIF":3.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750325000067","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In this manuscript, we present a new and efficient 6th-order compact difference method for solving the nonlocal Lane–Emden equation. This method effectively addresses singular-type problems without the need to modify or remove the singularities. To achieve this, we construct a uniform mesh across the domain and develop a new sixth-order discrete method that approximates the derivatives, transforming the differential equation into a system of equations. Use Newton or any iterative technique to obtain the numerical solution of the system of equations. Our new scheme efficiently handles the singularity at . Additionally, we conduct a mathematical analysis of the method’s consistency, stability, error bounds, and convergence rate. We also include several numerical problems from the existing literature to demonstrate the accuracy, efficiency, and applicability of the proposed scheme. Also, compare the numerical approximations with the existing recent techniques. The newly proposed scheme offers 6th-order accuracy using a small-size matrix and delivers better numerical results than the existing methods.
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).