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Modeling and Computational Engineering

This is the study programme for 2020/2021.

This course introduces numerical methods and modeling techniques used to solve practical problems. The course provides insights and skills in computational thinking and programming techniques
You will learn the most common numerical methods used to solve complex physical, biological, financial or geological phenomena. Examples of methods are numerically derivation, numerical integration, Monte Carlo and boot strapping methods, inverse methods, numerical solution of common differential equations, simulated annealing, and colony optimization, lattice Boltzmann models, random walk models, box (compartment) models.
The primary programming language is Python. Through assignments, you will learn how to set up mathematical models of a phenomenon, develop algorithms, implement them, and investigate the strength and limitations of the solution method and the mathematical model.

Learning outcome

  • Advanced knowledge in the use of algorithms and computational thinking to solve discrete and continuous problems
  • Understand the constraints associated with the chosen solution method, including approximation errors and constraints linked to the selection of specific algorithms or numerical methods.
  • In depth knowledge of the basic numerical methods

  • Develop models of physical systems from biology, chemistry, flow in porous media, and geology
  • Test models against experimental data, and use data to constrain the model
  • Apply computational thinking to solve mathematical models by the use of appropriate numerical methods
  • Develop own programs written in the program language Python

General Competence:
  • Visualize and presentation of results from numerical simulations
  • The use of computers to work more efficiently with large amounts of data

Required prerequisite knowledge


Recommended previous knowledge

MAF300 Numerical Modeling, MAT100 Mathematical Methods 1, MAT110 Linear Algebra, MAT320 Differential Equations
Matematiske Metoder 1 (MAT100), Linær Algebra (MAT110), Differensialligninger (MAT320), Numerisk Modellering Grunnkurs (MAF300)


Weight Duration Marks Aid
Folder evaluation1/1 A - F
Portfolio assessment:
The folder consists of three projects, of which all count 1/3 of the total grade. There is no written or oral examination. If a student fails or wants to improve the grade, he or she has to take course again.

Coursework requirements

Students must have passed one or two mandatory assignments in order to get a grade in the course.

Course teacher(s)

Course coordinator
Aksel Hiorth
Head of Department
Alejandro Escalona Varela

Method of work

Open to

Single Course Admission to PhD-courses


Literatur will be published as soon as it has been prepared by the course coordinator/teacher

This is the study programme for 2020/2021.

Sist oppdatert: 10.08.2020