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DRPS : Course Catalogue : School of Physics and Astronomy : Undergraduate (School of Physics and Astronomy)

Undergraduate Course: Modelling and Visualisation in Physics (PHYS10035)

Course Outline
SchoolSchool of Physics and Astronomy CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaUndergraduate (School of Physics and Astronomy) Other subject areaNone
Course website WebCT Taught in Gaelic?No
Course descriptionThis course covers the process of mapping a scientific problem onto a computer algorithm to enable it to be modelled, along with an introduction to Java graphics to help visualise the solution. Example problems will be drawn from the Junior Honours physics programme, with additional examples from 'everyday' problems.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Computational Methods (PHYS09016) OR Advanced Computer Simulation (PHYS10014)) OR ( Informatics 2B - Algorithms, Data Structures, Learning (INFR08009) AND Informatics 2C - Introduction to Software Engineering (INFR08019))
It is RECOMMENDED that students have passed Statistical Mechanics (PHYS09019) AND Electromagnetism (PHYS09018) AND Dynamics and Relativity (PHYS09014)
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 13/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 8, Seminar/Tutorial Hours 1, Supervised Practical/Workshop/Studio Hours 33, Summative Assessment Hours 3, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 53 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 50 %, Coursework 15 %, Practical Exam 35 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)3:00
Summary of Intended Learning Outcomes
Upon successful completion it is intended that the student will be able to:

1)Write complex simulation codes in Java.
2)Design and write simple visualisation software in Java, depicting evolving fields, moving particles, and graphs; interface this software to simulation codes
3)Design and write simple graphical user interfaces in Java.
4)Locate, understand, download and incorporate useful publically-available methods from outwith the course materials using the internet; appreciate the difference between reusable object-oriented coding and plagiarism
5)Understand and apply three major techniques of computer coding: integrating an equation, minimising a function and sampling from a distribution
6)Have completed simulations of molecular dynamics of many particles; cellular automata and the percolation transition; the Ising model for ferromagnetism and antiferromagnetic phase transitions; Maxwell's equations, understanding the usefulness of the vector potential; Develop a deeper understanding of these physical problems through simulation
7)Understand the notion of equilibration of a simulation, and efficient data-gathering, and their relation to simulation time
8)Appreciate the aspects of a code which limit computer performance, the conflict between object-oriented and computationally efficient code, and the occasions where each is to be preferred
9)Appreciate the importance of documentation and commenting in ensuring reusability; Write user guides in English to enable third parties to use the codes.
10)Have built a personal library of methods which will enable the student to complete a simple, unseen coding task effectively and quickly
Assessment Information
Checkpoint assessments based on computational laboratory tasks, 35%
Oral and/or written presentation of computational methods to class, 15%
Unseen practical examination in CP Lab, 50%.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus Theoretical background of core simulation techniques including: sampling from a distribution (Monte Carlo), solution of initial- and boundary-value partial differential equations, time evolution of deterministic and stochastic equations of motion
Implementation of these core techniques in Java to solve specific (and potentially unseen) physics problems
Integration of visualisation (evolving fields, moving particles, live graphs etc) and graphical user interfaces into simulation codes
The notion and origin of errors and instabilities in numerical algorithms, and simple techniques for handling them
Key issues that arise in the development of scientific software, such as: compromises between efficiency and flexibility, the incorporation of third-party library code (and its distinction from plagiarism) and the utility of good-quality documentation and coding style
Transferable skills Not entered
Reading list Not entered
Study Abroad Not entered
Study Pattern Not entered
Course organiserDr Davide Marenduzzo
Tel: (0131 6)50 5283
Course secretaryMs Dawn Hutcheon
Tel: (0131 6)50 7218
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