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2018/19 Undergraduate Module Catalogue

SOEE3250 Inverse Theory

10 creditsClass Size: 20

Module manager: Prof Andy Hooper
Email: a.hooper@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2018/19

Pre-requisite qualifications

Students require a solid background in university level maths (particularly matrix algebra)

This module is mutually exclusive with

SOEE5116MComputational Inverse Theory
SOEE5675MInverse Theory

This module is not approved as a discovery module

Module summary

Given a description of a physical system, we can predict the values of any measurements we might make. This is known as a forward problem. The inverse problem is to use actual measurements to constrain the values of the parameters that characterise the system. Various approaches can be taken to solve an inverse problem depending on the linearity of the forward problem, the form of the measurement errors, the non-uniqueness of solutions and the number of model parameters and observations. This module will cover how to characterize any specific problem and choose, then implement, an appropriate approach. Students will learn the theoretical basis behind different approaches and also put them into practice using MATLAB.

Objectives

After completing this module, students will be able to
1. Formulate inverse problems
2. Explain the difficulties inherent in inverse problems
3. Solve linear inverse problems using least-squares
4. Linearise and solve non-linear inverse problems
5. Describe and implement methods for regularization of ill-posed problems
6. Formulate inverse problems in terms of probability distributions
7. Solve inverse problems using Markov chain Monte Carlo algorithms

Syllabus

Formulation of inverse problems, linear least-squares, best linear unbiased estimator (BLUE), propagation of errors, maximum likelihood solutions, linearisation of non-linear problems, Monte Carlo error propagation, ill-posed problems, resolution matrix, regularization, cross validation, Bayesian inference, Markov chain Monte Carlo algorithms, neighbourhood algorithms.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture101.0010.00
Practical102.0020.00
Private study hours70.00
Total Contact hours30.00
Total hours (100hr per 10 credits)100.00

Private study

Completion of practical problems (10 x 2 hours).
Background reading for lectures (10 x 2 hours).
Exam preparation and revision (1 x 30 hours).

Opportunities for Formative Feedback

Continuous monitoring during practicals with immediate formative assessment and feedback. Coursework provides a mixture of summative (counts towards 20% of the final mark) and formative assessment. Assessments contain questions on topics that will be similar to those on the final exam.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
In-course AssessmentContinuous assessment20.00
Total percentage (Assessment Coursework)20.00

Resits will be assessed by a single 1.5 hour exam only (no coursework).


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)1 hr 30 mins80.00
Total percentage (Assessment Exams)80.00

Resits will be assessed by a single 1.5 hour exam only (no coursework).

Reading list

The reading list is available from the Library website

Last updated: 26/04/2017

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