Undergraduate Course: Fourier Analysis and Statistics (PHYS09055)
|School||School of Physics and Astronomy
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 9 (Year 3 Undergraduate)
|Home subject area||Undergraduate (School of Physics and Astronomy)
||Other subject area||None
||Taught in Gaelic?||No
|Course description||Full details to follow. It will be seen as a coherent 20pt course by all single honours physics students, with one three hour exam in the December diet. For joint honours it needs to be available as two 10pt halves, so that they can drop one half. For the half courses there are two hour exams in the December diet.
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Fourier Analysis (PHYS09054)
||Other requirements|| None
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||No
Course Delivery Information
|Delivery period: 2014/15 Semester 1, Available to all students (SV1)
||Learn enabled: No
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 22,
Formative Assessment Hours 3,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
||Hours & Minutes
|Main Exam Diet S1 (December)||Fourier Analysis and Statistics||3:00|
Summary of Intended Learning Outcomes
|- State in precise terms key concepts relating to Fourier analysis, the solution of partial differential equations and probability and statistics.
- Apply standard methods of Fourier analysis, the solution of partial differential equations and probability and statistics to solve physical problems of moderate complexity.
- Justify the logical steps in an extended solution of a physics problem.
- Use experience and intuition gained from solving physics problems to predict the likely range of reasonable solutions to an unseen problem.
- Resolve conceptual and technical difficulties by locating and integrating relevant information from a diverse range of sources (including computer algebra packages where appropriate).
|Coursework 20% and examination 80%.|
||Fourier Analysis (20 lectures)
- Linear algebra view of functions: orthonormal basis set, expansion in series
- Fourier series: sin and cos as a basis set; calculating coefficients; examples of waves; Complex functions; convergence, Gibbs phenomenon.
- Fourier transform; uncertainty principle.
- Solving Ordinary Differential Equations with Fourier methods.
- Applications of Fourier transforms: Fraunhofer diffraction; Quantum scattering; forced-damped oscillators; wave equation; diffusion equation.
- Alternative methods for wave equations: d'Alembert's method; extension to nonlinear wave equation.
- Convolution; Correlations; Parseval's theorem.
- Power spectrum; Sampling; Nyquist theorem.
- Dirac delta; Fourier representation.
- Green's functions for 2nd order ODEs.
- Sturm-Liouville theory: orthogonality and completeness.
Partial Differential Equations, Probability & Statistics (20 lectures)
- Overview of Partial Differential Equations in Physics: Poisson, Wave, Diffusion, Continuity, Laplace, Schrodinger.
- Separation of variables.
- Examples with rectangular symmetry: 'rectangular harmonics'.
- Examples with circular symmetry: Bessel functions.
- Examples with spherical symmetry: Legendre polynomials, spherical harmonics; Charged sphere, gravitational potential.
- Probability of discrete events; Multiple events: joint, conditional and marginal distributions.
- Bayes' theorem; frequentist view; probability as a degree of belief.
- Generalisation of probability to continuous variables.
- Permutations, combinations.
- Random walk and the binomial distribution; Stirling's approximation; Gaussian and Poisson distributions as limiting cases.
- Functions of a random variable: expectations, moments; Fourier transform of probability distribution as moment generating function; Application to sampling non-uniform random numbers.
- Addition of random variables as a convolution; addition of Gaussian distributions; central-limit theorem.
- Estimating mean, variance, error on the mean from finite data sets.
- Cumulative distribution and centiles; error function; hypothesis testing, confidence limits.
- Least squares fitting; goodness of fit; x2 distribution; maximum likelihood; improbably good and poor fits.
- Residuals; error analysis; Kolmogorov-Smirnov test.
- Linear regression; correlations.
|Course organiser||Prof John Peacock
Tel: (0131) 668 8390
|Course secretary||Mrs Bonnie Macmillan
Tel: (0131 6)50 5905
© Copyright 2014 The University of Edinburgh - 29 August 2014 4:37 am