Lecturer: Andreas Dedner

Term(s): Term 2

Status for Mathematics students: List C

Commitment: 30 one hour lectures

Assessment: 100% by 3 hour examination

Formal registration prerequisites: None

Assumed knowledge:

Useful background:

Synergies:

Content:

This module will introduce students to the analysis and numerical approximation of variational solutions of partial differential equations (PDEs). Many mathematical models are based on PDEs but their complexity generally make it impossible to solve these explicitly. Existence and uniqueness of solutions to these problems can be shown based on their variational formulation but this on the face of it, does not provide a practical way of evaluating solutions. In this module we will discuss how the variational formulation can be used to construct so called Galerkin approximations. The most commonly used form of these is the finite element method. We will discuss both implementational and analytical aspects of these method in this module.

We will start with an overview of variational formulations of PDEs and the relevant function spaces so that MA4A2 (Advanced PDEs) is not a prerequisite. Some results from functional analysis will only be recalled, e.g., Riesz representation and Lax-Milgram theorems.

This is an indicative module outline only to give an indication of the sort of topics that may be covered.

• Sobolev spaces and their main properties in connection with variational formulations of PDEs
• Galerkin approximations of variational problems
• Introduction to implementational aspects of finite element methods
• Approximation theory and a-priori error analysis
• Derivation of a-posteriori error estimators
• How the concepts are used to solve non-linear and time dependent problems

Learning Outcomes: By the end of the module students will:

• have an overview of Sobolev spaces and their main properties
• understand the fundamental concepts of Galerkin approximations to weak solutions of linear PDEs
• implement finite-element methods to compute approximations
• carry out a-priori error analysis based on the Bramble-Hilbert theory
• understand the difference between a-priori and a-posteriori analysis and how to obtain a-posteriori error estimators.
• understand how the Galerkin method can be used to solve non-linear and time dependent problems

Books:

• Lawrence C Evans Partial Differential Equations AMS
• Dietrich Braess, Finite Elements: Theory, Fast Solvers, and Applications in Solid Mechanics (Cambridge University Press
• S Brenner and L Ridgeway Scott, The mathematical theory of finite element methods Springer Texts in Applied Mathematics Volume 15.
• Michael Reed and Barry Simon An Introduction to Partial Differential Equations Springer.

Additional Resources