This Ebook is designed for science and engineering students taking a course in numerical methods of differential equations. Most of the material in this Ebook has its origin based on lecture courses given to advanced and early postgraduate students. This Ebook covers linear difference equations, linear multistep methods, Runge Kutta methods and finite difference methods for elliptic, parabolic and hyperbolic equations.
As a course in numerical analysis it contains a variety of finite difference schemes and efficient algorithms implemented in mathematics.
MATH6623 - Numerical Methods for Differential Equations
The mathematical modulae attached to each chapter with solutions of practical examples should help readers to understand the text and apply the methods. It is expected that the readers will find theorems with proofs and applications interesting and informative. List of Mathematica Modulae. Chapter 1 Linear Difference Equations. Chapter 2 Solution of Ordinary Differential Equations. Chapter 3 Finite Difference Method.
Chapter 4 Elliptic Equations. Chapter 5 Parabolic Equations.
Introduction to Numerical Methods in Differential Equations | Mark H. Holmes | Springer
Chapter 6 Hyperbolic Equations. Recommend this eBook to your Library This Ebook is designed for science and engineering students taking a course in numerical methods of differential equations.
Most of the material in this Ebook has its origin based on lecture courses In this section we describe some of the behaviour that should be expected of methods in general and, in subsequent sections, indicate how this behaviour can be designed into LMMs. Thus, convergent methods generate numerical solutions that are arbitrarily close to the exact solution of the IVP provided that h is taken to be sufficiently small.
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Since non-convergent methods are of little practical use we shall henceforth assume that all LMMs used are convergent—they are consistent and zero-stable. In this chapter we describe the use of LMMs to solve systems of ODEs and show how the notion of absolute stability can be generalized to such problems.
We begin with an example. The discussion of absolute stability in previous chapters shows that it can be advantageous to use an implicit LMM—usually when the step size in an explicit method has to be chosen on grounds of stability rather than accuracy.
Lecture Notes in Numerical Methods of Differential Equations
One then has to compute the numerical solution at each step by solving a nonlinear system of algebraic equations. Runge—Kutta RK methods are one-step methods composed of a number of stages. All the methods discussed thus far have been parameterized by the step size h. There are many applications where one is concerned with the long-term behaviour of nonlinear ODEs.
It is therefore of great interest to know whether this behaviour is accurately captured when they are solved by numerical methods. Thus far the emphasis in this book has been focused firmly on the solutions of IVPs and how well these are approximated by a variety of numerical methods. This attention is now shifted to the numerical method primarily LMMs and we ask whether the numerically computed values might be closer to the solution of a modified differential equation than they are to the solution of the original differential equation. At first sight this may appear to introduce an unnecessary level of complication, but we will see in this chapter as well as those that follow on geometric integration that constructing a new ODE that very accurately approximates the numerical method can provide important insights about our computations.
It is perfectly natural to.
This led us, in earlier chapters, to the concepts of global error and order of convergence. However, there are other senses in which approximation quality may be studied.
A Theoretical Introduction to Numerical Analysis
We have seen that absolute stability deals with long-time behaviour on linear ODEs, and we have also looked at simple long-time dynamics on nonlinear problems with fixed points. In this chapter and the next we look at another well-defined sense in which the ability of a numerical method to reproduce the behaviour of an ODE can be quantified—we consider ODEs with a conservative nature—that is, certain algebraic quantities remain constant are conserved along trajectories. This gives us a taste of a very active research area that has become known as geometric integration , a term that, to the best of our knowledge, was coined by Sanz-Serna in his review article .
The material in these two chapters borrows heavily from Hairer et al. This chapter continues our study of geometric features of ODEs.
We look at Hamiltonian problems, which possess the important property of symplecticness. As in the previous chapter our emphasis is on. Many mathematical modelling scenarios involve an inherent level of uncertainty. For example, rate constants in a chemical reaction model might be obtained experimentally, in which case they are subject to measurement errors.
Or the simulation of an epidemic might require an educated guess for the initial number of infected individuals. More fundamentally, there may be microscopic effects that a we are not able or willing to account for directly, but b can be approximated stochastically.