TEK-8004 Fundamentals of Scientific Computing - 5 stp

Can be taken as a single course.

Who has to apply for admission?

• PhD students at UiT that wishes to take a PhD level course which is not going to be a part of the PhD degree they study for at UiT.

• PhD students from other institutions than UiT.

• Applicants that has achieved a master’s degree (five year master or three year bachelor degree + two year master degree), but isn’t a PhD student.

• This course is aimed at PhD candidates from engineering disciplines, but is open to postdoc fellows if seats are available.
• PhD candidates from both UiT and NORDTEK universities may be admitted to the course. PhD candidates not registered at UiT must upload a document from their university stating that they are registered PhD candidates.

If there are 5 applicants or fewer, the course responsible will evaluate whether the course will take place.

Upon successful completion of the course, the following learning outcomes are expected to be achieved:

Knowledge:

After passing the course, the student is expected to be able to:

• Knowledge of concepts and definitions in the mathematics of modeling, numerical analysis, and optimization.
• Knowledge of formulating results covered by the course.
• Knowledge of theories in scientific computing cases.
• Understanding the solution methods in applied mathematics.

Skills:

After passing the course, the student is expected to be able to:

• Interpret concepts in scientific computing;
• Describe the main features (and proofs) of the main theorems;
• Apply the scientific computing methods to solve problems in applied mathematics.

Competence:

• After passing the course, the student is expected to be able to use computation to solve problems in engineering science by applying mathematical models.

Lectures and problem-solving sessions.

Acquisition (face-to-face presentations and lectures, demos, reading paper books).

Investigation (using text-based study guides, analyzing the ideas and information in a range of materials & resources, searching and evaluating information and ideas, using digital resources for scaffolding, using digital tools to collect and analyze data).

Practice ("field" trips, using models, simulations, practicing exercises, doing practice-based projects).

Discussion (tutorials, seminars, class discussions, online tutorials)

Collaboration (individual project, online forums, wikis, chat rooms, etc.).

Production (reports, models, oral exam).