Advanced Computer Simulation (U01368)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : PHY-3-CompSim

This is a practical course which develops the techniques of computer simulation in physics through the exploration of specific examples. It consists of an introduction to Monte Carlo integration, a study of the numerical integration of simple dynamical systems, and a look at some non-numerical computational methods for computer symbolic algebra. The course is taught through a series of two-hour supervised practical classes in the Computational Physics Laboratory. The course is continuously assessed: there is no Degree Examination.

Entry Requirements

? Pre-requisites : Physics 2A: Forces, Fields and Potentials (PHY-2-A); Foundations of Mathematical Physics (PHY-2-FoMP) or Applicable Mathematics 4 and Mathematical Methods 4 (MAT-2-am4/mm4) or Principles of Mathematical Physics (PHY-2-PoMP); Computer Simulation (PHY-2-CompSim) or Computer Science 2B (INF-2-CS2B) or Computational Methods (PHY-3-CompMeth); prior Java experience is essential.

? Prohibited combinations : Concurrent attendance at Computational Methods (PHY-3-CompMeth) is not permitted. However, students are permitted to take Advanced Computer Simulation (PHY-3-CompSim) after having passed Computational Methods (PHY-3-CompMeth).

Variants

? This course has variants for part year visiting students, as follows

Advanced Computer Simulation (VS1) (Semester 1 (Blocks 1-2))

Subject Areas

Home subject area

Undergraduate (School of Physics), (School of Physics, Schedule Q)

Delivery Information

? Normal year taken : 3rd year

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 3 hour(s) per week for 11 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
19/09/2006	16:00	17:00	Lecture Room 3315, JCMB	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	16:10	18:00	KB

Summary of Intended Learning Outcomes

After completing this course students should:
1) be familiar with the properties of floating point arithmetic, rounding errors, errors due to algorithmic
approximations, basic (Euler)numerical integration methods and simple higher-order integrators (leapfrog);
2) have learnt about the principles of Monte Carlo integration, including importance sampling, simple
methods of generating pseudo random numbers for specified distributions, but not Markov Chain methods;
3) have an understanding of the techniques used to implement computer algebra systems, including the use of
recursion, linked lists, garbage collection, and markup languages such as MathML;
4) have a deeper understanding of the utility and
limitations of derived classes, interfaces, and inheritance in object-oriented programming languages (specifically Java);
5) be familiar with the use of documentation generator tools (specifically JavaDoc).