Main field(s) of study and in-depth level:
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
G1N: has only upper-secondary level entry requirements
G1F: has less than 60 credits in first-cycle course/s as entry requirements
G1E: contains specially designed degree project for Higher Education Diploma
G2F: has at least 60 credits in first-cycle course/s as entry requirements
G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
GXX: in-depth level of the course cannot be classified.
A1N: has only first-cycle course/s as entry requirements
A1F: has second-cycle course/s as entry requirements
A1E: contains degree project for Master of Arts/Master of Science (60 credits)
A2E: contains degree project for Master of Arts/Master of Science (120 credits)
AXX: in-depth level of the course cannot be classified.
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
The Faculty Board of Science and Technology
Ordinary Differential Equations I or corresponding course, Scientific Computing I
This course aims to provide insight and practice in how dynamical system (i.e. differential equation and difference equation) models can be used to better understand biology, physics, biochemistry, economics and sociology. The focus will be on formulating models, model analysis, using numerical solution tools to better understand models and drawing conclusions based on model outcomes. Since the course contents is rather wide a focus will be made on increasing overall confidence in mathematical modelling, rather than deep study of each particular area. On completion of the course, the student should be able to:
formulate important models treated during the course;
outline the mathematical methods and techniques that are used to analyse these models and understand in what situations these methods can be applied;
understand how to draw a conclusion from a model;
use a computer package to investigate models numerically;
solve standard problems within the areas covered by the course.
The course will consist of first an overview/revision of the theory of ordinary differential and difference equation models. This will be followed by a series of cases studies. In each case study there will be an emphasis both on how to build a model of various systems (i.e. applications), mathematical analysis (i.e. techniques), and on how numerical solutions increase understanding. Case studies At least three case studies will be chosen from the following possibilities. Solving the case studies will involve a combination of mathematical analysis and numerical solution in MATLAB. 1, Molecular and cellular biology. Non-dimensionalisation; Michaelis-Menten kinetics; matched asymptotic expansions; models of neural firing; oscillations in biochemical systems (Britton 6.1-6.4; Strogatz 8.3). 2, Coupled oscillators. Flows on the circle; driven and coupled pendulums; global bifurcations; firefly flashing; Kuramoto model (Strogatz 4.5-4.6 and 8.4-8.7 and further material) 3, Biological motion. Introduction to partial differential equations; diffusion equation; Fisher's equation; travelling wave solutions; reaction-diffusion equations and pattern formation; Turing bifurcations (Britton 5; Britton 7). 4, Chaos. Population dynamics and one-dimensional maps. Cobweb diagrams; periodic windows; Liapunov exponent
Lectures, problem solving and computer laboratories.
Written examination combined with assignments given during the course.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.