Module: Numerical Methods Laboratory

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Numerical Methods Laboratory
Module Code	PS358
School	School of Physical Sciences
Module Co-ordinator	Semester 1: Karsten Fleischer Semester 2: Karsten Fleischer Autumn: Karsten Fleischer
Module Teachers	Albert Ellingboe Karsten Fleischer
NFQ level	8	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat the module
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Description

To introduce the student to advanced experimental and numerical techniques in the areas of Optics, Solid State Physics, Instrumentation, Process Control, but also chemistry, biology and for societal problems To enhance the students understanding of concepts presented in lectures. To provide the student with training in; good laboratory and coding practice; data and error analysis; data presentation and report writing.

Learning Outcomes

1. Carry out advanced level physics experiments.
2. Write an advanced computer program, in an appropriate computer language, to simulate/model/demonstrate a physics concept.
3. Identify the connection between experiment and theory and apply advanced theoretical physics concepts to the analysis of experimental data.
4. Record data in a systematic manner and maintain a laboratory notebook.
5. Produce a detailed written report, including correctly formatted tables, graphs and diagrams
6. Perform a detailed and comprehensive error analysis of experimental data.
7. Students will be aware of ethical issues with regard to plagiarism
8. Present results from computational and laboratory experiments

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Online activity	20	Carrying out virtualized experiments
Laboratory	20	Carrying out in-presence real experiments
Online activity	30	Writing of python programs for the computational experiments. Either in the computer lab in DCU or online from home.
Tutorial	5	Tutorial sessions to address computational difficulties students might face (in person but with live streaming/remote support)
Online activity	15	Collaborative coding on the computational project, either in small groups in the lab or fully online (student choice!)
Online activity	20	Preparing for experiments, pre-lab presentations and going through accompanying "lecture" material
Independent Study	70	Write-up of reports
Assignment Completion	7.5	Create and present the poster on one experiment (of any form) of choice
Total Workload: 187.5

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

Computational Experiments
The student carries out five computational experiments. Three are common with other cohorts: using the Euler, Runge Kutta, and graphical methods to solve differential equations numerically. Two are specific to this module and are selected from the following list: Principal Component Analysis (PCA), comparison of advanced data analysis techniques, Monte Carlo simulations, virtual experiments, chaotic systems modelling.

Experimental Laboratory
The student will carry out two real experimental projects some (10 hours each) The experimental projects will be selected from the following list: The Zeeman Effect, Birefringence/Transmission, Blackbody Radiation, Michelson and Mach Zhender Interferometer, The lock-in Amplifier, Optoelectronic Detectors, Shockley-Haynes effect, Magnetic damping.

Computational group project
The students employ numerical methods to explore a physical, chemical, or biological system governed by differential equations. These are small group (2-3 students) projects. Examples are the solar dynamo, Paul trap, Brusselator reaction, the heart beat, but also topical projects such as spread of an infection.

Accompanying lecture material
The lab is accompanied with online teaching material on important issues of laboratory and data analysis in general. This includes error progression, evaluation of correlation and uncertainties of least square fitting, as well as material on numerical modelling, software design and development, and use of version control systems.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Presentation	Brief 10min pre-lab presentations for real life labs to demonstrate preparedness for the more complex experiments in term of underlying theory, experimental techniques employed	10%	As required
Presentation	15min presentation of an experiment of choice (virtual, or real lab experiment) to other students, illustrating the theory behind an experiment and main outcomes and uncertainties encountered.	5%	As required
Report(s)	Provide reports on the experiments (computational, virtual or real) including the theoretical background, results and findings, as well as error discussion	60%	As required
Poster presentation	Present a poster on a selected computational, real or virtualised experiment	5%	Sem 1 End
Completion of online activity	Online MCQs on accompanying material (error progression, fitting, experimental methods)	20%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 2

Indicative Reading List

Other Resources

None

Programme or List of Programmes

PAN	BSc Physics with Data Analytics

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