BERN01 Modelling in Computational Science, 7.5 credits, is a compulsory course at advanced level for a degree of Master of Science in Computational Science and for a degree of Master of Science in Applied Computational Science. The course can be included as an optional course within a master's degree in Mathematics and it can also be given as an independent course. The course is given at half-study pace during the first half of the autumn semester. The course is given in English.

Course Content
The course gives an overview of fundamental modelling techniques in computational science. An introduction to mathematical modelling of problems in the sciences and to how these models can be treated using computational methods is given. In particular, differential equation based models, machine learning for databased models, and Monte-Carlo methods for statistical models, are discussed.

The course treats

Differential equation based modelling: Numerical methods to solve ODEs and
convergence order. Applications to for instance predicting the spread of diseases
and cell programming will be used.
MCMC based modelling: Central Limit Theorem, Metropolis-Hasting algorithm
and error propagation will be covered with examples from statistical
thermodynamics.
Machine learning and big data: We will cover the use of large data sets for creating models by training machine learning algorithms and which conditions are needed to create such models. This will be carried out on climate science applications.

Teaching
The teaching consists of lectures. Three compulsory projects carried out in small groups are included in the course. Attendance at all oral group presentations of the
project results is mandatory.

Assessment
The examination takes place through oral presentations of the group programming projects as well as opposition to another group's reports. Attendance at all presentations is compulsory.

Course Literature

Official Course Description

Course Evaluation