An efficient method for solving coupled viscous Burgers’ equations

In this paper, we propose a Reproducing Kernel Method (RKM) for solving the system of Coupled Viscous Burgers’ Equations (CVBEs). The accuracy and efficiency of the RKM depend critically on several choices: the component space, inner product, discretization points, basis functions, and overall solution strategy. As these elements are interdependent, suboptimal selections in any component can hinder the desired results. The inherent complexity of solving nonlinear PDE systems, further amplified by the structural requirements of the RKM, makes developing an effective method for this problem class particularly challenging. A further objective is to establish a systematic theoretical framework encompassing error analysis, convergence theorems, and supporting lemmas that align closely with numerical outcomes. The proposed method must also demonstrate superiority over existing approaches. Our numerical experiments confirm that these challenges have been successfully addressed, showcasing the method’s efficacy and validating the theoretical framework.

Authors

Institutions

Publication Details

Journal
Discover Applied Sciences
Published
2026-03-02
DOI
https://doi.org/10.1007/s42452-026-08440-4
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An efficient method for solving coupled viscous Burgers’ equations

S. Abbasbandy, Hussein Sahihi, Taher Amoozad, Tofigh Allahviranloo
Discover Applied Sciences
Matrix Theory and Algorithms
article

An efficient method for solving coupled viscous Burgers’ equations

S. Abbasbandy, Hussein Sahihi, Taher Amoozad, Tofigh Allahviranloo
article en

Abstract

In this paper, we propose a Reproducing Kernel Method (RKM) for solving the system of Coupled Viscous Burgers’ Equations (CVBEs). The accuracy and efficiency of the RKM depend critically on several choices: the component space, inner product, discretization points, basis functions, and overall solution strategy. As these elements are interdependent, suboptimal selections in any component can hinder the desired results. The inherent complexity of solving nonlinear PDE systems, further amplified by the structural requirements of the RKM, makes developing an effective method for this problem class particularly challenging. A further objective is to establish a systematic theoretical framework encompassing error analysis, convergence theorems, and supporting lemmas that align closely with numerical outcomes. The proposed method must also demonstrate superiority over existing approaches. Our numerical experiments confirm that these challenges have been successfully addressed, showcasing the method’s efficacy and validating the theoretical framework.

Discover Applied Sciences
Islamic Azad University, Science and Research Branch (IR), Imam Khomeini International University (IR)
Openalex Percentile: Top 36%
Matrix Theory and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.