# MODERN COMPUTATIONAL TECHNIQUES - 2017/8

Module code: PHY3042

Module provider

Physics

Module Leader

STEVENSON PD Dr (Physics)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 6

JACs code

F343

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Assessment pattern

Assessment type | Unit of assessment | Weighting |
---|---|---|

Coursework | COURSEWORK ASSIGNMENT #1 | 40% |

Coursework | COURSEWORK ASSIGNMENT #2 | 60% |

Alternative Assessment

N/A

Prerequisites / Co-requisites

PHY2063: Energy, Entropy and Numerical Physics

Module overview

This module covers techniques in solving physics problems on computer in a more advanced way than lower-level courses. It combines use of scientific program libraries within low-level languages, with advanced algorithms, and parallel processing techniques, and introduces the high-level Maple package for analytic computation, with applications to solving differential equations.

Module aims

To advance skills in different areas of using computers to solve physics problems.

To gain experience and confidence in using the BLAS and LAPACK libraries to perform common linear algebra operations, applied to solving physics problems involving differential equations

To learn advanced algorithms and techniques, such as the FFT algorithm, the use of neural networks, monte carlo techniques, and numerical minimization and optimization. To use the FFTW library.

To gain experience and understanding of the use and practice of parallel programming.

To visualise the output of these computations

to equip students with sufficient familiarity with Maple to solve differential equations with it.

Learning outcomes

Attributes Developed | |
---|---|

Understand how to use scientific computational libraries in general, and the BLAS and LAPACK libraries in particular, and how to apply them to physics problems | KPT |

Appreciate the utility of Monte Carlo methods and make decisions about where to apply them | KC |

Evaluate when and where it is appropriate to use parallel programming techniques, to be able to then do so, and evaluate the increase in efficiency. | KCPT |

Understand and implement the Fast Fourier Transformation Algorithm | KPT |

Employ the anaytic Maple package for the solution of differential equations. | CPT |

Attributes Developed

**C** - Cognitive/analytical

**K** - Subject knowledge

**T** - Transferable skills

**P** - Professional/Practical skills

Module content

Indicative content includes:

• Differential equations in physics; Linear algebra approach to their solution via BLAS & LAPACK libraries; Introduction to Maple and application to series, integration, and solution of equations (4 weeks)

• Algorithmic techniques: Optimisation; Neural Networks, the Fast Fourier Transform (3 weeks)

• Monte Carlo Methods (2 weeks)

• Parallel programming: OpenMP and MPI. Benchmarking and parallel algorithms (2 weeks)

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Build the skills and knowledge of students to apply advanced computational techniques to problems of physical relevance, and to be able to select appropriate tools for unseen problems

The learning and teaching methods include:

Two-hour lecture session in which material is developed on the board in the first hour, followed by an hour in which the developed ideas are implemented computationally

Weekly one-hour classes in the computer lab with specific short formative excercises to help understand each week’s material

Use of online discussion board

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate ability to apply the range of techniques used in this course to tackle problems of relevance in broad areas of physics.

Thus, the summative assessment for this module consists of:

two assignments which feature programming exercises, covering the whole range of course material

The first assignment is due on Tuesday of Week 7 at 4pm and covers Linear Algebra approach to differential equation via BLAS/LAPACK and Maple.

The second assignment is due on Tuesday of Week 12 at 4pm and covers the remainder of the material.

Formative assessment

Weekly exit tests.

Feedback

Each week's material features a short exit test which can be submitted for feedback. The first summative assignment will be returned with feedback during the teaching weeks. A discussion board provides written feedback to written questions, and the live sessions offer instant feedback.

Reading list

Reading list for MODERN COMPUTATIONAL TECHNIQUES : http://aspire.surrey.ac.uk/modules/phy3042

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.