Module: Computational Physics

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Computational Physics
Module Code	PS432 (ITS) / PHY1063 (Banner)
Faculty	Science & Health	School	Physical Sciences
Module Co-ordinator	Miles Turner
Module Teachers	-
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Repeat examination

Description

This module introduces final year undergraduate students to the concepts and techniques of computational physics, in the context of studies in physical sciences. In particular, students should understand that the method proceeds by (i) identifying a suitable physical model expressed in mathematical terms, (ii) finding an algorithm by which the model can be solved, (iii) implementing the solution, and (iv) calculating the desired results. In addition to this basic approach, the module will also discuss modern ideas about the challenges of establishing that the correct solution has been found, and the opportunities and difficulties of exploiting high performance computer hardware for large scale problems. The continuous assessment element of the module will include practical exercises, which will assume a working knowledge of a suitable computer language, such as Python or MatLab.

Learning Outcomes

1. Develop and articulate a systematic understanding and knowledge at the forefront of the field of computational physics both qualitatively, and quantitatively of the fundamental and underpinning elements of the computational approach
2. Demonstrate this systematic understanding and knowledge by developing and articulating a mathematical model in a form suitable for computational solution
3. Select from complex and advanced techniques across this field of learning to identify suitable tools for generating the computational solution (e.g. algorithms, computer languages, hardware)
4. Demonstrate this systematic understanding and also demonstrate a critical awareness of the main issues and modern insights around establishing that the computed solutions are correct, based on modern ideas on verification and validation
5. Demonstrate a critical awareness of the challenges and opportunities of high performance computation for large scale problems, the boundaries of the learning in the field and the preparation required to push back those boundaries through further learning

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Workshop	24	8 x 3 hour workshops
Tutorial	6	6 x tutorials
Independent Study	95	Study for lectures (24 hours), study for tutorials (6 hours), work on design and implementation of computational solutions for assessment problems (problem 1 - 20 hours; problem 2 - 20 hours), preparation for examination (25 hours).
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Basic Concepts
General ideas on the role of computation in the physical sciences. The process of developing a computational model, from mathematical formulation to developing and testing the solution. Ideas about critical evaluation of solutions.

Mathematical Models
Formulation of mathematical models, beginning with conceptualising a physical model and proceeding to a formal mathematical model, including identifying relevant boundary conditions and parameters.

Computational Tools
Choosing a computational approach based on the mathematical character of the model. Algorithms for ordinary and partial differential equations. Monte Carlo methods.

Software Tools
Computer languages for scientific problems. Libraries and other software packages.

Verification and Validation
Criticism of the computational approach. Recent ideas on establishing the correctness of scientific computations and physical models.

Assessment Breakdown
Continuous Assessment	40%	Examination Weight	60%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Designing and implementing a computational solution to a problem involving an ordinary differential equation	20%	n/a
Assignment	Designing and implementing a computational solution to a problem involving Monte Carlo techniques	20%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

William H. Press: 2007, Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, New York, 978-052188068
William L. Oberkampf, Christopher J. Roy: 0, Verification and Validation in Scientific Computing, Cambridge University Press, 9780521113601

Other Resources

None