Undergraduate Course: Simulation, Analysis, and Validation of Computational Models (INFR11254)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | We will study advanced computational methods to model, simulate, and solve complex problems in engineering, science, and society. The course provides an introduction into the field of scientific computing from an interdisciplinary viewpoint and will enable a well-informed use of models, methodologies, and software in a variety of use cases. We will discuss dynamics, control, optimisation, risk, and resilience of systems as well as reliability, scalability, validation and verification of models. The course will be useful for work on real-world challenges in domains such as robotics, digital twins, supply chains, and manufacturing. |
| Course description |
The course relies on the insight that all data are observations of an underlying dynamical systems and that data can be used more efficiently when supported by dynamical models. In order to help to realise this potential, the course offer an environment to engage with the following topics:
- methods of qualitative and quantitative modelling
- stability and the role of feedback in dynamics systems
- analysis of stochasticity and response to perturbations
- resilience and trustworthiness
- modelling strategies and explainability by design
- case studies on various levels of complexity
- principles of validation, verification, and credibility
- outlook at current research and challenges
The course will be delivered by weekly lectures that are accompanied by opportunities for self-study to revise your background in calculus, programming, and critical thinking in the first three weeks. A central part of the course are three workshops that provide hands-on experience in modeling, analysis and visualisation, which will be useful towards the coursework that will run over the last weeks of the teaching period.
The intended audience are those wanting the background required to begin research and development of computing science methods in various application areas.
|
Information for Visiting Students
| Pre-requisites | It is recommended that students pass the following courses:
Introduction to Linear Algebra (MATH08057)
Calculus and its Applications (MATH08058)
Discrete Mathematics and Probability (INFR08031)
OR comparable prior learning experiences. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Supervised Practical/Workshop/Studio Hours 6,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 70 %
Practical Exam _____% (for courses with programming exams)
Coursework 30 %
Coursework will involve using, modifying, testing, and evaluating software that is introduced in the course. Results will be described and discussed in a coursework report. Projects will be defined by the course team, but students can also choose to work on a self-proposed project for the coursework. |
| Feedback |
Feedback will be given in workshops in immediate connection to the problems with which the students are engaging.
Feedback on coursework will be given in a session soon after the submission of the coursework.
Written feedback will be given as part of coursework marking.
A weekly drop-in Q&A session will be offered in from week 3.
The course will have an online discussion forum. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- understand the process of conceptualizing complex systems
- design frameworks for simulation of such systems
- to use simulation software in practical use cases
- assess and mitigate requirements of computational power for simulations
- understand processes for model validation and verification
|
Reading List
Rongpeng Li, Aiichiro Nakano (2022) Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social Sciences, Apress.
Claus Führer (2021, 2nd ed.) Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas. Packt Publishing.
James S. Walker (2007) Ullmann's Modeling and Simulation, Wiley VCH.
Claus Beisbart, Nicole J. Saam (eds.) (2019) Computer Simulation Validation Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives
Winter Simulation Conference archive https://informs-sim.org |
Additional Information
| Graduate Attributes and Skills |
The students will be able to:
- Engage in activities related to modeling complex systems in a variety of contexts.
- Undertake critical evaluations of a wide range of modeling frameworks and simulation systems.
- Apply critical analysis, evaluation and synthesis to forefront issues, or issues that are informed by forefront developments in computational science.
- Identify, conceptualize, and define interdisciplinary real-world problems and issues.
- Develop original and creative responses to such problems and issues.
- Critically review, consolidate and extend knowledge, skills, practices and thinking related to the topic of the course.
- Deal with complex issues and make informed judgments in situations in the absence of complete or consistent data or information. |
| Keywords | Simulation,Dynamics,Modeling,Computing,Systems Thinking,SAVM |
Contacts
| Course organiser | Dr Michael Herrmann
Tel: (0131 6)51 7177
Email: Michael.Herrmann@ed.ac.uk |
Course secretary | |
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