Applied Computational Modelling (MOD300)

The course will give a practical introduction to modeling. We will develop models for supporting decision making in complex and uncertainty contexts, physical phenomenon and processes, and others relevant to different engineering disciplines. The mathematical models are, for example, models for decision analysis, statistical models, models of physical systems, machine learning models, and hybrid models where machine learning methods are combined with physical models. The numerical methods of solving the models will be introduced. We will focus on practical applications in the form of constructing models and implementing specific algorithms to solve practical problems with the purpose of supporting and improving decision making. Coding/programming will be an important part of the course, and we will use Python as the primary programming language for the course.

Course description for study year 2022-2023


Course code




Credits (ECTS)


Semester tution start


Number of semesters


Exam semester


Language of instruction


Learning outcome

Everyone makes decisions, but few people think about how they do it. Yet, psychological research shows that we are prone to many different errors of thought that degrade our decision quality. A fundamental role of engineers is to build physical and mathematical models for supporting decision-making. Models are useful and valuable if and only if they can generate insights that can guide and inform decision-making. In this course, we will discuss how to develop normative models for decision making under uncertainty and models describing conservation of mass, energy, and chemical components and how to practically implement numerical analysis techniques. The end goal for the model is to support and improve decision making. Examples of the numerical analysis techniques are regression and interpolation techniques (for both single and multiple variables), data filtering, Monte Carlo simulation, and machine learning.

Required prerequisite knowledge


Recommended prerequisites

DAT120 Introduction to Programming, MAF310 Numerical Modeling, STA100 Probability and Statistics 1


Form of assessment Weight Duration Marks Aid
Prosjektoppgave 1/1 Letter grades

3-5 project assignments with reports. Assessment details will be revised and described before the course is offered for the first time in fall 2023.

Course teacher(s)

Course coordinator:

Aksel Hiorth

Course coordinator:

Alejandro Escalona Varela

Method of work

Lectures and practical programming exercises.

Course assessment

Form and/or discussion.


Search for literature in Leganto