AI Summary & Key Takeaways
AI InsightTheproposed scheme and the residual correction technique are illustrated in several example problems.
📌 3 Key Takeaways
Abstract
In this paper, we present a weighted residual Galerkin method to solve linear functional differential equations.We consider the problem with variable coefficients under initial conditions. Assuming the exact solution of the problemhas a Taylor series expansion convergent in the relevant domain, we seek a solution of the given problem in the form of apolynomial having degree N of our choice. Substituting this polynomial with unknown coefficients in the given equationyields an expression linear in these coefficients. We then proceed as in the weighted residual method and take innerproduct of this expression with monomials up to degree N , resulting in N + 1 linear algebraic equations. Appropriatelyincorporating the initial conditions and solving the resulting linear system, we obtain the approximate solution to thegiven problem. Additionally, we present a way of estimating the absolute error of the obtained approximation, which isthen used to improve the original approximation through a method called residual correction. We also show that theupper bound for the error of the proposed method depends on the Taylor truncation error of the exact solution. Theproposed scheme and the residual correction technique are illustrated in several example problems.