School of Mathematics and Physics

AMA Level 3 Modules

  • AMA3001 Electromagnetic Theory (1st semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
The ideas of electromagnetic theory are developed at a comparatively sophisticated mathematical level making extensive use of the methods of vector calculus. The module develops the subject from the simplest situation - charges at rest (electrostatics), through charges in uniform motion (magnetostatics) to charges in non-uniform motion (electrodynamics). The full Maxwell equations appropriate to the latter general situation are built up from simpler forms, with their origins in experimental findings.

Students completing this module will understand the mathematical treatment of electromagnetism and through solving the problems, will have experience in mathematical modelling. They will be fluent in the use of vector calculus. The methods used here find application in more advanced studies of theoretical physics.

Contents
Electrostatics: electric field; Gauss's law; Poisson's equation; Laplace's equation; polarisation; electric displacement; boundary conditions in electrostatics; methods for solving Laplace's equation; electrostatic energy density.
Steady currents: electric current; equation of continuity.
Magnetostatics: magnetic scalar and vector potentials; magnetic dipole; magnetic field; magnetisation; boundary conditions in magnetostatics; potential problems.
Electromagnetic induction: electromotive force; magnetic energy; energy density of the magnetic field.
Maxwell's equations: electromagnetic energy; electromagnetic potentials; the wave equation; plane waves; electromagnetic radiation; wave-guides.

Assessment


Exam 70%   Assignments 30%

  • AMA3002 Quantum Theory (1st semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
Together with relativity, quantum mechanics defines our understanding of nature gained throughout the 20th century and beyond. Though the development of quantum theory is one of the greatest intellectual achievements this introductory course does not require advanced mathematical skills or advanced knowledge of physics.

After a brief historical survey, the discussion of Young double-slit experiment and the Planck-Einstein-de Broglie relations, we introduce the mathematical foundations of quantum mechanics, introducing Dirac notation and the state space. We then discuss to the Postulates of quantum theory, giving particular emphasis to the Heisenberg uncertainty principle and the Schrödinger equation, which is derived and discussed in depth. To illustrate its key role in quantum theory, we address some exactly solvable simple problems, including the motion of a particle in various potentials, the spin, a simple harmonic oscillator, a particle in a central potential field and the hydrogen atom. The last part of the module is dedicated to the introduction of the perturbative technique to address problems that cannot be solved exactly (they are the majority, actually). We thus develop both the time-independent and the time-dependent perturbation theory, and apply it to a number of important (yet simple) problems.

Although the course is largely self-contained and has no specific prerequisites from Level 2, advantage would be gained by taking AMA2001 Classical Mechanics and AMA2003 Methods of Applied Mathematics.

Contents
Introduction to the fundamentals of quantum mechanics: Historical context; light quanta and Planck-Einstein relations; Young's double slit experiment; de Broglie relations; wave functions (introduction).
Dirac notation and state space: notion of ket and bra; linear operators; Hermitian conjugation and Hermitian operators; representation of kets/bras and operators; position and momentum representations; tensor products of state spaces (with applications).
Postulates of quantum mechanics: statement of the postulates (including measurement process); physical interpretation of the postulates (measurement of observables, mean and standard deviation, compatibility of observables, Heisenberg uncertainty principle, time-energy uncertainty); derivation of Schrödinger equation and its physical interpretation (determinism of the unitary evolution, Ehrenfest principle, constants of motion, stationary states); superposition principle and physical predictions (probability amplitudes and interference, linear superpositions, degenerate eigenvalues).
Applications of the postulates I: free particle and particle in a box (infinite potentials; finite-well potential; step potential, including a quick introduction to scattering).
Applications of the postulates II: Spin-1/2 particle.
Applications of the postulates III: Quantum harmonic oscillator.
Applications of the postulates IV: Angular momentum in quantum mechanics.
Applications of the postulates V: Central potential (hydrogen atom).
Time-independent perturbation theory and application to simple problems.
Time-dependent perturbation theory and application to simple problems.

Assessment

Exam 70%      Team Project 30%

  • AMA3003 Tensor Field Theory (2nd semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or a BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathway, and a mathematical knowledge and ability commensurate with this stage is assumed. Useful background includes vector algebra and dynamics, linear algebra, vector calculus, fluid mechanics, calculus of variations, classical mechanics, electromagnetism, quantum mechanics. 

Introduction

Physical theories are associated with a particular mathematical machinery, often catalyzing each other’s development. For example, electromagnetism goes with vector calculus, quantum mechanics - with linear algebra, etc. Tensor analysis is the machinery of general relativity, but has applications in many other areas. Tensors are a generalization of vectors and matrices. Their defining property is how they behave under coordinate transformations. Their usefulness is that they represent intrinsic, coordinate-independent properties and relations. This module covers the algebra and calculus of tensors, with applications in special and general relativity. The mathematician will enjoy the tensors and differential geometry, and the theoretical physicist - the relativity; this module appeals to a range of backgrounds and interests.

Contents
Tensor algebra: Coordinate transformations; scalar; gradient; contravariant and covariant vectors; tensor field; outer and inner product; contraction; quotient rule; metric tensor; symmetry; scalar product, length, orthogonality.

Special relativity and electromagnetism: Lorentz transformation; velocity and acceleration transformation; time dilation; Lorentz contraction; proper time; Minkowski space-time; 4-velocity; 4-acceleration; 4-momentum; 4-force; mass-energy-momentum relations; scattering; Maxwell’s equations; gauge transformation; 4-current; 4-potential; electromagnetic tensor; Maxwell’s equations in tensor form; transformation of fields; electromagnetic energy tensor.

Tensor calculus: Curvature; geodesics; Christoffel symbols; covariant derivative; divergence and Laplacian; geodesic coordinates; curvature tensor; Bianchi identity; Ricci tensor; Einstein tensor.

General relativity: Newtonian gravity; energy-momentum tensor; Einstein field equations; weak-field approximation; Schwarzschild solution; tests of general relativity; black holes. 

Assessment

Exam 80%    Continuous Assesssment 20%

  • AMA3006 Partial Differential Equations (1st semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
This module develops further some ideas and methods initially introduced in AMA1002. It concerns those problems in Applied Mathematics which can be formulated as differential equations involving functions of more than one variable (e.g., position and time, or several coordinates). These are partial differential equations (PDE). Besides AMA1002, students deal with examples of such equations in AMA2005, AMA3001, AMA3002, AMA3007, and SOR3012. This course studies properties of these equations, and develops methods for solving them.

Contents
Introduction: Terminology: order, linear and nonlinear equations, initial and boundary conditions. Derivation of the wave equation (in one dimension, 1D) and heat equations. Further examples: Laplace's equation, Poisson's equation, Schrödinger equation. Method of Separation of Variables: for the wave equation and heat equation in 1D, and for the circular membrane. Bessel functions. Eigenvibrations (modes).
Fourier Method: Fourier expansion of a function. Piecewise continuous functions. Convergence of Fourier series (FS). FS for even and odd functions. Half-range FS. FS near discontinuities - the Gibbs phenomenon. Application of FS to solving PDE. Laplace's equation for a disk: Poisson's integral.
Integral Transform Methods: Fourier transform (FT) as a limit of the complex Fourier series for the infinite interval. Notion of the Dirac delta function. FT of even and odd functions, sine and cosine FT, and FT of partial derivatives. Application to PDE (heat, wave, Laplace). Laplace transform (LT). LT of some common functions, convolution and shift theorems. Applications of LT to ordinary and partial differential equations.
Orthogonal expansions: Sturm-Liouville Theory. Inner product and norm, orthogonal systems of functions. Gram-Schmidt process. Expansion of functions in orthogonal systems. Convergence in the mean and completeness. Self-adjoint differential operators and Green's formula. Singular, periodic and homogeneous boundary conditions. Sturm-Liouville theory: properties of the eigenfunctions and eigenvalues; degeneracy. Generalised Fourier series. Fourier-Bessel expansion.
Green's Functions: Dirac delta function. Green's function of the Sturm-Liouville equation. Green's functions in several dimensions (Dirichlet problem for the Laplace equation; heat and wave equations with source terms).
Normal Forms of 2nd-order PDE in Two Variables: Linear and quasi-linear PDE. Hyperbolic, parabolic and elliptic types. Reduction to the normal form, and use of this method for solving PDE.

Assessment

Exam 80%   Project 20%

  • AMA3007 Financial Mathematics (2nd semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
Mathematical skills are highly sought after in the financial services industries, and this employment sector remains a favoured destination for graduates. Around 40% of Mathematics graduates entering employment across the UK (see www.prospects.ac.uk for recent data) go into financial services, which includes, accountancy, retail and investment banking, mergers and acquisitions, insurance and actuarial work, capital market trading, and hedge fund employment, and so on.

At the low end of this sector, retail banking for example, a degree in mathematics is certainly not essential. This work is mainly concerned with simple arithmetic operations. However, at the high end of financial services, in a hedge fund for example, employers expect to see PhD-level qualifications in mathematics from their applicants along with excellent software skills. These mathematicians are involved in the business of derivative pricing and trading and earn salaries well over 100k. Derivatives are financial products (instruments as they are called in the trade) derived from assets that have an unpredictable price. The total outstanding notional value of derivatives contracts today has grown beyond a quadrillion dollars (that's 1015 to you and me). It is a perilous and lucrative business!

Derivatives were originally devised to avoid risk by providing an insurance on a risky asset. Nowadays, they are an essential part of risk taking in capital markets. Indeed the speculation in buying and selling these instruments, specifically credit derivatives, precipitated the current credit crunch. Of course, this trade relies upon knowing the fair price of a derivative. Pioneering work by Black, Merton and Scholes, showed that, under certain assumptions for the unpredictability of the asset, the price of the derivative obeys a partial-differential equation. The construction of such equations and their solution is where mathematicians come in!

The objective of the course is to provide an introduction to the mathematical techniques which can be applied to pricing problems for financial derivatives. Specifically, our focus is on stochastic calculus and the theory and practice of pricing simple derivatives such as contracts and options.

We are grateful to First Derivatives plc for their support of this course and the provision of prizes for the best examination performance.

Contents
Introduction to financial derivatives: forwards, futures and options. Future markets and prices. Option markets. Binomial models and the risk-free portfolio. Stochastic calculus and random walks. Black-Scholes equation. Pricing European options. Various option pricing models. Interest-rate derivatives. Credit derivatives. Swaps.

Assessment

Exam 70%   Report 20%   Presentation 10%  

  • AMA3011 Applied Mathematics Project (1st or 2nd Semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Contents

Self-study of an advanced mathematical topic under the supervision of a member of staff. Students will be offered a choice of subjects, which can span the entire range of applied mathematics, including theoretical physics. The study concludes with a written report and an oral presentation.

Assessment


Report  80%   Presentation 20%

  • AMA3013 Calculus of Variations and Hamiltonian Mechanics (2nd semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics/Applied Mathematics and Physics/Theoretical Physics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
The aim of this course is to develop the essential ideas and mathematics of the Calculus of Variations, with applications to various problems.

In its simplest form the Calculus of Variations is concerned with finding a function which makes a given integral a maximum or minimum, or, more generally, ‘stationary’. A famous historical example (solved by the Bernoullis, Newton and others) is the following. A particle, which is initially at rest, slides under gravity along a smooth wire connecting two points: find the shape of the wire, which minimizes the time taken for the particle to travel between the two points. This corresponds to finding the function which minimizes the time integral. More generally, ‘variational’ problems may involve integrals containing more than one function or higher derivatives of the functions than the first, or involve constraints on the functions, which may be given in integral form or point-wise. In addition, the variational integral may be multi-dimensional.

In the formulation of physical laws variational methods have a much deeper significance. This was first suggested by the elegant work of Lagrange and the Irish mathematician Hamilton who looked at the basic mathematical structure of classical mechanics. The formalism that they developed is not only essential for a full understanding of quantum mechanics and statistical mechanics but turns out to have a much wider application in that it can be extended to systems that are not normally considered in dynamics, e.g., the Electromagnetic Field. In this sense variational methods provide a ‘unifying’ principle of physics.

The first part of this course concentrates on the basic mathematics of the Calculus of Variations. The second part deals with Lagrangian and Hamiltonian mechanics and their variational basis. While the course is more or less self-contained, it does requires a knowledge of basic first-year calculus, including functions of several variables and a smattering of ordinary differential equations. Newton's laws of motion in vector form and the elementary ideas of kinetic and potential energy are also needed.

Contents
Part I: The Calculus of Variations (approximately 15 lectures): Motivation: the Brachistochrone and Isoperimetric problems. Functional; extremum, stationary point. Function classes. Weak and strong variations. The simplest variational problem: necessary condition for an extremum; Euler-Lagrange lemma; Euler's equation. Several unknown functions. Fermat's principle. Geodesics. Functionals depending upon higher order derivatives. Variational problems with subsidiary conditions: Lagrange multipliers; finite subsidiary conditions. Variable end-point theorem: broken extremals; Weierstrass-Erdmann corner condition. Second variation of a functional: Legendre's necessary condition for a minimum. Direct methods: the Ritz method; the method of finite differences; the Sturm-Liouville problem.
Part II: Analytical Mechanics (approximately 15 lectures): Constraints and generalized coordinates; holonomic constraints. Virtual displacement; D'Alembert's principle; Lagrange's equations. Action integral. Hamilton's principle. Generalized momentum; cyclic coordinates; conservation laws. The Hamiltonian; Hamilton's equations; derivation of Hamilton's equations from a variational principle. Principle of least action; Jacobi's form of the principle of least action. Canonical transformations: generating function; symplectic matrices and canonical transformations; Hamilton's equations in symplectic form. Poisson brackets: Jacobi's identity; canonical transformations of Poisson brackets; Hamilton's equations in Poisson bracket form; Poisson's theorem. The Hamilton-Jacobi equation; Hamilton's characteristic function. Action-angle variables. Phase-space diagram.

Assessment

Exam 70%   Project 30%

  • AMA3014 Mathematical Modelling in Biology and Medicine (2nd semester)

Pre-requisite: While there are no specific pre-requisites, this course is intended for students at stage 3 of either an MSci or BSc Mathematics pathways, and a mathematical knowledge and ability commensurate with this stage is assumed.

Introduction
Mathematics has always benefitted from its involvement with developing sciences. Biomedical science is clearly the premier science of the forseeable future. With the example of how mathematicians have have benefited from and influenced physics, it is clear that if mathematicians become involved in the biosciences they will be part of a field of important and exciting scientific discoveries.

In this module no previous knowledge of biology is assumed. With each topic a brief description of the biological background will be provided sufficient to understand the models being studied. The emphasis throughout the course is on the practical application of mathematical models in helping unravel the underlying mechanisms involved in the biological processes. The mathematics in the module is dictated by the biology and not vice-versa.

By the end of the course the student should have a good appreciation of the art of modelling which relies on: (i) a sound understanding and appreciation of the biological problem; (ii) a realistic mathematical representation of the important biological phenomena; (iii) finding useful solutions, preferably quantitative; and finally what is crucially important (iv) a biological interpretation of the mathematical results in terms of predictions and insights.

Contents
The module will cover models used in population biology and ecology. These are listed below.
1. Continuous population models for single species.
2. Discrete population models for single species.
3. Models for interacting populations.
4. Biochemical genetics.
5. Biological motion including reaction diffusion models and chemotaxis.
6. Biological waves for single species models.

Assessment

Exam 50%   Computer Modelling 30%   Report 20%

  • AMA3020      Investigations     (2nd semester)

Pre-Requisites:           

    

Course Content: 

This module provides an introduction to project development and management in topics of Applied Mathematics. Students are trained in research methods by working on a range of projects.

In the first part of the module the students conduct a short practice investigation, followed by two short investigations (one in small groups [typically in pairs] and one individually) in a range of problems in Applied Mathematics and Theoretical Physics. The results are presented in the form of typed reports.

This is followed by a long investigation, which is a literature study each a topic in Mathematical or Theoretical Physics not covered in the offered (or chosen) modules. The results of the investigation are presented in the form of a set of notes and a presentation that takes the form of a short lecture and is delivered by the students individually.

The two short investigations are typed up in reports and submitted for assessment. The set of notes on which the presentation for the long investigation is based, is typed up and submitted for assessment.

 

Assessment:                  Reports x2 80%   Group Presentation 20%

  • AMA3021 Computational Finance (1st semester)

Pre-requisite: Only available to students on the BSc Mathematics with Finance programme.

Introduction
This module should equip the students with the knowledge of mathematical, analytical and numerical techniques applied to capital markets. It will allow them to develop the ability to work with market data to visualise, capture and use software to analyse financial data. It will also provide the students with knowledge of random process, Markov chains and time-series and will give them capacity to develop software and perform regression analysis of time-series. Upon completion the students will have skills in the application of numerical methods to analyse financial data

Contents
Capital Financial Markets: Financial institutions. Money markets and foreign exchange. Bond market and spreads. Equity Markets. Forwards and Futures. Swaps and Options. Trading platforms. Financial data analysis: Discrete random walks and Markov processes. Central-limit theorem. Time-series analysis. Stationarity. Autocorrelation and regression. Curve fitting and goodness-of-fit. ARIMA. Volatility and GARCH. Simulation: Theory of arbitrage. Monte Carlo simulation. Bond pricing. Value-at-Risk. Default. Modern portfolio theory and optimisation. Capital asset pricing model. Derivatives: Fundamental theorem of asset pricing. Options and Futures. Binomial models of pricing European options.

Assessment

Class test 50%   Report 33%  Continuous assessment 17%

  • AMA3022 Team Project: Mathematics with Finance (2nd semester)

Pre-requisite: Only available to students on the BSc Mathematics with Finance programme.

Introduction
As a result of taking this module, students will learn to respond to a briefing on a problem by a client. They will be able to work successfully as part of a team to address the problem. They will also be able to make a final presentation on the outcome of the work.

Contents
Business skills workshop. Presentation skills. Negotiation skills. Customer relationship. Project management/team building. Teams required to negotiate, plan, develop and deliver a completed task working as a group, commissioned by the `client' company. The project will require software development skills.

Assessment

Business Proposals 30%    Report & Presentation 40%    Business Plan 10%   Peer Evaluation 20%