MA999-15 Topics in Mathematical Modelling
Introductory description
N/A.
Module aims
This is a core module for the Mathematics for Real-World Systems II CDT renewal. The aim is to introduce students to cutting-edge topics in mathematical modelling that cover the application areas of the CDT: biomedical science, epidemiology, socio-technical systems, and industrial processes and optimization. The topics covered will be used as examples to illustrate fundamental modelling approaches, in particular multiscale modelling and hybrid modelling, which bridges the divide between a priori and data-driven methods .
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The module will provide practical examples of the two research themes of the MathSys II CDT: (1) Multiscale Modelling and (2) Hybrid Modelling. These will be demonstrated using practical examples taken from the application areas of the CDT: quantitative biomedical research, mathematical epidemiology, socio-technical systems and advanced modelling and optimisation of industrial processes. Students will learn how to relate and apply the skills learned in the first-term core modules and understand how the various theoretical methodologies can be used to solve real-world problems. The concepts utilised will include: symmetries and constraints, phase transitions, stochastic and deterministic modelling, data-driven modelling, agent-based modelling, course graining, non-linearities and bifurcations and probability.
Learning outcomes
By the end of the module, students should be able to:
- The students will learn how to use the knowledge and techniques they have acquired in the other core modules of the MSc programme to tackle particular mathematical challenges which are related to real world problems of partners of the CDT.
- Apply mathematical modelling techniques taught in the core of the course to cutting-edge research questions.
- Get an overview over a research area and give a critical summary and list of interesting problems.
- Understand and present a recent research paper in the context of the mathematical background of the MSc cohort.
- Should be acquainted with 4 current research areas within the CDT which will guide them in choosing MSc and PhD projects.
Indicative reading list
Recent research papers in the field of the 4 lecturers. Will be made available well in advance when the module is advertised to the students.
Subject specific skills
See learning outcomes.
Transferable skills
Students will acquire key reasoning and problem solving skills which will empower them to address new problems with confidence.
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 3 hours (20%) |
| Tutorials | 10 sessions of 1 hour (7%) |
| Private study | 110 hours (73%) |
| Total | 150 hours |
Private study description
Self-study.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A2
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Assessed Coursework | 100% | Yes (extension) |
Feedback on assessment
Coursework will receive written feedback. Additional oral feedback available from lecturer and TA during problem classes.
Courses
This module is Core for:
- Year 1 of RMAA-G1PG Postgraduate Research Mathematics of Systems
-
TMAA-G1PF Postgraduate Taught Mathematics of Systems
- Year 1 of G1PF Mathematics of Systems
- Year 1 of G1PF Mathematics of Systems
This module is Optional for:
- Year 2 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)
- Year 1 of TESA-H1B1 Postgraduate Taught Predictive Modelling and Scientific Computing
This module is Option list B for:
- Year 1 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)