Course content

Numerical solution of

definite integrals

ordinary differential equations and systems of such

the diffusion equation

non-linear algebraic equations

the method of least squares

High-level scientific computing for

visualisation of multi-dimensional data

linear algebra

optimisation

statistics, combinatorics, and random numbers

interpolation

signal and image processing

machine learning

Learning outcome

After completing the course, the candidate will be able toKnowledge:

describe, explain and derive methods for numerical solution of selected problems

assess which methods to use for solving a given problem, and analyse the accuracy of the methods

Skills:

implement numerical algorithms in suitable high-level languages

apply high-level languages for scientific computing

apply numerical methods for solving practical problems

General competence:

document methods and results from scientific computations in the form of technical reports, with suitable use of figures, tables, equations, cross references, and bibliography

Learning methods and activities

Forelesninger|Obligatoriske oppgaver

Obligatoriske arbeidskrav:

2 compulsory assignments

Compulsory assignments

• Approved exercises

Specific conditions

Recommended previous knowledge

IMT1082 Object-Oriented Programming

IMT2021 Algorithmic Methods

REA2101 Physics and Chemistry

Required previous knowledge

TØL1001 Introduction to Engineering

IMT1031 Fundamental Programming

REA1141 Mathematics 1

REA2091 Mathematics 2 for Computer Science or REA2081 Mathematics 2 for Electical Engineering

Course materials

A. Tveito, H. P. Langtangen, B. F. Nielsen, X, Cai: Elements of Scientific Computing. Springer, 2010EuroScipy tutorial team: Python Scientific lecture notes. http://scipy-lectures.github.com.