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School of Mathematics

# MATH46111/MATH66111 - 2007/2008

General Information
• Title: Numerical Functional Analysis
• Unit code: MATH46111/66111
• Credits: 15
• Prerequisites:
• Co-requisite units: None
• School responsible: Mathematics
• Members of staff responsible:
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## Specification

### Aims

To develop understanding of functional analysis. To establish existence and uniqueness of solutions for a number of important mathematical models. To derive numerical algorithms for PDEs and prove rigorous error estimates. To introduce ideas from probability and the study of stochastic PDEs.

### Brief Description of the unit

This course unit develops the rigorous analysis of PDEs with special emphasis on numerical approximation. After developing a number of tools in functional analysis, we consider the well posedness of some PDEs of mathematical physics: we formulate precise definitions of solution and show these solutions exist and are unique. We go on to develop numerical algorithms and prove rigorous estimates in simple cases. We look at finite differences for ODEs, the Galerkin approximation for the Poisson equation and a spectral/finite difference approximation to the heat equation. We conclude with an introduction to stochastic PDEs, which is currently a very active area of research.

### Learning Outcomes

On successful completion of this course unit students will

• understand the concept of Banach and Hilbert spaces and some of their theory;
• understand the importance of existence and uniqueness theory and how it is developed for ODEs, the Poisson equation, and the heat equation;
• be able to understand the finite difference and finite element approximations and prove error estimates hold;
• be familiar with some stochastic PDEs and how the above theory is extended to the stochastic case.

None.

### Syllabus

1. Introduction. Well posedness. Review of Cauchy sequences. Banach and Hilbert spaces. [2 lectures]
2. ODEs. Existence of unique solution for the initial value problem. Lipschitz property. Contraction Mapping Theorem.
Gronwall inequality. Convergence of Euler discretisation. [5 lectures]
3. Model Diffusion Problem. Sobolev spaces and generalised derivatives. Classical and weak solutions of model diffusion problem. Existence and uniqueness of solution. Riesz Representation Theorem. [6 lectures]
4. Galerkin Approximation and the Finite Element Method. Classical and weak solutions. Galerkin approximation and convergence. Finite element spaces. [5 lectures]
5. Linear Operators. Bounded linear operator. Symmetric and compact operators. Integral operators. Eigenfunctions and complete orthonormal basis. Hilbert Schmidt Theorem. Application to Sturm Liouville problem. Unbounded operators. [6 lectures ]
6. Further topics. Introduction to Galerkin approximation of stochastic PDEs [3 lectures]

### Textbooks

• Howard Elman, David Silvester and Andy Wathen, Finite Elements and Fast Iterative Solvers, Oxford University Press, Oxford 2005, ISBN 0-19-852868-X (paperback).
• James C. Robinson, Infinite Dimensional Dynamical Systems, Cambridge, 2001.
• Michael Renardy and Robert Rogers, An Introduction to Partial Differential Equations, Springer 1993.

### Teaching and learning methods

27 lectures (two or three lectures per week) with a weekly examples class. In addition should expect to spend at least seven hours each week on private study..

### Assessment

Mid-semester coursework: 25%
End of semester examination: three hours weighting 75%

## Arrangements

On-line course materials for this course unit.