Module Title:   Advanced Numerical Methods

Module Credit:   10

Module Code:   ENG4031M

Academic Year:   2015/6

Teaching Period:   Semester 1

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Engineering

Related Department/Subject Area:   Engineering: Mathematics and Computing (not in use)

Principal Co-ordinator:   Prof SJ Shepherd

Additional Tutor(s):   -

Prerequisite(s):   ENG2027M     ENG2028M

Corequisite(s):   None

To provide knowledge, understanding and skills for the critical evaluation and implementation of effective and efficient numerical methods for the solution of mathematical models of engineering problems.

Learning Teaching & Assessment Strategy:
Theory, implementation, application and critical analysis gained through lectures, tutorials and directed study, together with engineering application and evaluation gained from lab sessions, assessed by coursework.

Lectures:   12.00          Directed Study:   76.00           
Seminars/Tutorials:   6.00          Other:   0.00           
Laboratory/Practical:   6.00          Formal Exams:   0.00          Total:   100.00

On successful completion of this module you will be able to...

critically evaluate advanced numerical algorithms for algebraic and matrix systems;.

On successful completion of this module you will be able to...

select, justify, implement, and interpret appropriate numerical techniques for problems set in engineering contexts and notation;

On successful completion of this module you will be able to...

collate, manage and interpret data, and apply IT skills, systematic problem-solving skills and creative problem-solving strategies.

  Coursework   100%
  Course work

Outline Syllabus:
*Linear systems. *Scalars, vectors, matrices, tensors. *Formulating engineering problems in matrix terms. *Eigen-systems: Interpretation and numerical computation of eigenvalues and eigenvectors. *Orthogonalisation - Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Noise reduction, Feature extraction, the Pisarenko algorithm. * PROJECT 1 - Noise reduction in telecommunications signal processing (stationary data) and in the financial markets (non-stationary data). The real world! -- Non-linear systems - handling non-linear and non-stationary data. *Non-linear computing, neural networks, multi-dimensional expansion of Taylor series. *PROJECT 2 - numerical design of a neural network fo rnon-liner control application (elec/mech/civil engineering) and stock market prediction (financial engineering).

Version No:  5