Course syllabus adopted 2026-02-18 by Head of Programme (or corresponding).
Overview
- Swedish nameMatematisk modellering för globala system
- CodeMEE135
- Credits7.5 Credits
- OwnerTKGBS
- Education cycleFirst-cycle
- Main field of studyGlobal systems, Mathematics
- DepartmentENVIRONMENTAL AND ENERGY SCIENCES
- GradingUG - Pass, Fail
Course round 1
- Teaching language Swedish
- Application code 74131
- Open for exchange studentsNo
- Only students with the course round in the programme overview.
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0126 Written and oral assignments 7.5 c Grading: UG | 7.5 c |
In programmes
Examiner
- Luisa Ickes
- Associate Professor, Geoscience and Remote Sensing, Space, Earth and Environment
Eligibility
General entry requirements for bachelor's level (first cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Specific entry requirements
The same as for the programme that owns the courseApplicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Course specific prerequisites
Computational tools, Single variable calculus, Linear algebra.The earth system (or similar course) and Applied mathematical thinking (or similar course)
Aim
Mathematical models are used in science and engineering to describe and represent different objects and systems, to analyze, understand and predict, and for finding the best design or strategy. Mathematical modelling is therefore a basic engineering skill.The problems in the course are taken from several different areas in order to make it possible to identify patterns in models and modeling across different fields of application. The course includes both solving smaller problems using self-developed models and working with existing larger and more complex models. An important part of the course involves working with climate models and understanding their possibilities and limitations.
Learning outcomes (after completion of the course the student should be able to)
- Describe different types of models and their characteristics, as well as modelling and problem-solving processes and how these models can be applied to different technical and scientific areas of application.
- Define and explain fundamental concepts in climate modelling, such as the climate system, climate models, climate forecasts, climate projections, climate scenarios, and climate sensitivity.
Skills and Abilities:
- Analyse and formulate mathematical models for real-world problems, including simplifying and making the problem precise, making and justifying assumptions, and selecting suitable mathematical representations.
- Analyse and solve complex problems using an exploratory, iterative, and systematic approach.
- Analyse and interpret model results.
- Design and carry out small-scale modelling projects, from problem formulation to conclusions, including implementing simple simulations or analytical tools.
- Apply technical and computational tools (e.g. CDO, Jupyter Notebook) to process, analyse, and interpret climate model data in a representative example.
- Present and communicate modelling results clearly and coherently, and justify methodological choices and conclusions both orally and in writing.
Judgement and Approach:
- Critically evaluate models with respect to uncertainties, assumptions, and methodological choices.
- Reflect on their own modelling processes and problem-solving strategies, and integrate relevant prior knowledge in problem-solving.
Content
The core of the course consists of application-oriented and open-ended problems that serve as a starting point for the students own learning. Through an investigative and iterative approach, students develop skills in formulating problems, selecting appropriate models, and analysing and interpreting model results. Particular emphasis is placed on understanding the possibilities, limitations, and uncertainties of models.
The problems cover several areas of application and involve different types of mathematical models, including:
- Functions and equations, for example how mathematical relationships can be motivated and how functions can be selected and fitted to empirical data.
- Optimisation models, for example mathematical programming in economics and decision support.
- Dynamic models, for example simulation in biology, physics, and engineering.
- Stochastic and discrete models.
Organisation
The course is organized in modules. For every module there is problem set and lectures adapted to the content of the modules. Some modules include workshops and small projects.
The learning is supported by an interactive way of teaching with interaction between students and teachers. This occurs during supervision hours or workshops. With guidance students will develop their own independent problem-solving abilities.
The learning is supported by an interactive way of teaching with interaction between students and teachers. This occurs during supervision hours or workshops. With guidance students will develop their own independent problem-solving abilities.
Literature
Distributed material and web-resources. Published and presented in detail on the course home page.
Examination including compulsory elements
The examination is based on a combination of:
- Written submissions to modules.
- Short quizzes on central concepts.
- Active participation in workshops, project presentations and other joint activities.
- Some parts of the course may be examined orally.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.