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Advanced Computational Laboratory - PHY00029H

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  • Department: Physics
  • Module co-ordinator: Dr. Yvette Hancock
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2024-25

Module summary

The Advanced Computational Laboratory runs in Semester 1 of each academic cycle. It trains students at solving advanced computational problems based on current research areas including ‘hot-topics’. It consists of 3 experiments through which the students extend their computational modelling skills and apply their problem-solving skills to the computational and scientific challenges each experiment contains. Experiments have guiding scripts, but are all open-ended leaving students with the possibility of pursuing their own scientific curiosity. Challenges include mastering the given software and or writing own codes to solve the problems proposed; learning to professionally keep laboratory log books; understanding of mathematical models; writing a full-fledged report in the style of a scientific article.

All experiments require to design, perform and analyse computational experiments. Previous computational experience is necessary for this course together with an appropriate level of knowledge of a computational language. No computational language or coding basics is taught during this course.

Related modules

Pre-requisite modules

  • None

Co-requisite modules

  • None

Additional information

Prereqs: (New modules) Advanced Mechanics, Computational Laboratories & Skills or equivalent

Module will run

Occurrence Teaching period
A Semester 1 2024-25

Module aims

  • The Advanced Computational Laboratory trains students at solving advanced computational problems based on current research areas including ‘hot-topic’.
  • It comprises 3 experiments conducted either in small groups or singularly.
  • It aims to extend students’ computational modelling skills and challenge their problem-solving skills to solve the scientific problems at hand. Indeed experiments have an open-ended component leaving students with the possibility of pursuing their own scientific curiosity.
  • Challenges include: mastering given software and writing own code to address the physical problem; writing a mini-report in the style of a long scientific abstract; learning to professionally keep laboratory log books; writing a full-fledged report in the style of a scientific article. Support and directions are given as appropriate, but previous computational experience is necessary for this course together with an appropriate level of knowledge of a computational language. No computational language or coding basics is taught during this course.

Module learning outcomes

  • the design, coding, and testing of codes of computer simulations based on advanced theoretical models of complex physical systems and of their behaviour
  • analytical skills pertaining to the physical interpretation and validation of numerical results, i.e., accuracy, correctness and limitations of the simulation model
  • the use of external libraries, such as the LAPACK library eigensolver, and/or other advanced simulations codes
  • a working understanding of the required physics, mathematical formalism and computational methods for studying the proposed physical problems
  • the analysis, design and interfacing of new and old code
  • the development of group-work skills pertaining to modern software development practices
  • keeping a 'working' laboratory logbook, which is updated concurrently as the laboratory progresses (individually and as part of a group project)
  • extended, scientific report writing, literature research/comparison and critical assessment of the literature
  • design, code, and test computer simulations based on advanced theoretical models of complex physical systems and of their behaviour
  • apply analytical skills pertaining to the physical interpretation and validation of numerical results, i.e., accuracy, correctness and limitations of the simulation model
  • utilise external libraries, such as the LAPACK library eigensolver, and/or other advanced simulations codes
  • demonstrate a working understanding of the required physics, mathematical formalism and computational methods for studying the proposed physical problems
  • the analysis, design and interfacing of new and old code
  • the development of group-work skills pertaining to modern software development practices
  • keeping a 'working' laboratory logbook, which is updated concurrently as the laboratory progresses (individually and as part of a group project)
  • extended, scientific report writing, literature research/comparison and critical assessment of the literature

Module content

The Advanced Computational Laboratory runs in Semester 1 of each academic cycle. It trains students at solving advanced computational problems based on current research areas including ‘hot-topic’. It consists of 3 experiments through which the students extend their computational modelling skills and apply their problem-solving skills to the computational and scientific challenges each experiment contains. Experiments have guiding scripts, but are all open-ended leaving students with the possibility of pursuing their own scientific curiosity. Currently, one of the experiments sees the students working in small groups to solve a material design problem using an existing materials simulation programme. Challenges include mastering the given software to address the physical problem and writing a mini-report in the style of a long scientific abstract. The other two experiments are pursued individually: solving the two-dimensional Ising model and a machine-learning problem. Challenges include writing own codes to solve the problems proposed and learning to professionally keep laboratory log books. In one of the experiment students will learn to use a mathematical model, the tight-binding model, to model graphene nanoribbons. Challenges include the study and understanding of this mathematical model, coding, and writing a full-fledged report in the style of a scientific article.

All experiments require to design, perform and analyse computational experiments. Previous computational experience is necessary for this course together with an appropriate level of knowledge of a computational language. No computational language or coding basics is taught during this course.

Assessment

Task Length % of module mark
Essay/coursework
Dissertation including code
N/A 40
Essay/coursework
Extended Abstract
N/A 15
Essay/coursework
Laboratory notebooks
N/A 45

Special assessment rules

Non-compensatable

Additional assessment information

For the Advanced Computational Laboratory, further information pertaining to assessments will be made available in the laboratory handbook, and assessment pro forma provided on the VLE, including the assessment breakdown for each lab-related assessment component, assessment criteria, as well as the submission/return-of-assessment deadlines within the laboratory timetable.

Reassessment

None

Module feedback

'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments.

A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback. This can be found at: https://www.york.ac.uk/students/studying/assessment-and-examination/guide-to-assessment/

The School of Physics, Engineering & Technology aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. Students are provided with their examination results within 25 working days of the end of any given examination period. The School will also endeavour to return all coursework feedback within 25 working days of the submission deadline. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each semester provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.

Our policy on how you receive feedback for formative and summative purposes is contained in our Physics at York Taught Student Handbook.

Indicative reading

Laboratory Manual; Laboratory Scripts; Scientific literature as appropriate to the proposed experiments



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.