Syllabus for Scientific Computing II

Beräkningsvetenskap II

A revised version of the syllabus is available.

Syllabus

  • 5 credits
  • Course code: 1TD395
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle
    G1N: has only upper-secondary level entry requirements
    G1F: has less than 60 credits in first-cycle course/s as entry requirements
    G1E: contains specially designed degree project for Higher Education Diploma
    G2F: has at least 60 credits in first-cycle course/s as entry requirements
    G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    GXX: in-depth level of the course cannot be classified.

    Second cycle
    A1N: has only first-cycle course/s as entry requirements
    A1F: has second-cycle course/s as entry requirements
    A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    AXX: in-depth level of the course cannot be classified.

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2007-03-15
  • Established by: The Faculty Board of Science and Technology
  • Applies from: week 27, 2007
  • Entry requirements: Scientific computing I.
  • Responsible department: Department of Information Technology

Learning outcomes

To pass, the student should be able to

  • describe the fundamental concepts discretisation, accuracy and order of accuracy, efficiency, stability, discretisation errors (truncation error), ansatz, adaptivity;
  • generally explain the idea behind the algorithms that are presented in the course;
  • describe the fundamental difference between stochastic and deterministic algorithms;
  • analyse the order of accuracy and stability properties for basic numerical methods and understand how such an analysis is employed;
  • evaluate methods with respect to accuracy, stability properties and efficiency;
  • based on such evaluation, discuss the suitability of methods given different different applications;
  • given a mathematical model, solve problems in science and engineering by structuring the problem, choose appropriate numerical method and generate solution using software and by writing programming code;
  • present, explain, summarise, evaluate and discuss solution methods and results in a short report.

Content

Continued programming in MATLAB, with specialisation in visualisation. Continued problem solving methodology. Data analysis: least squares problems with solution based on the normal equations and QR decomposition. Interpolation with an emphasis on piecewise interpolation (including cubic spines). Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods and in connection with this solution to non-linear equation systems. Monte Carlometoder and methods based on random number.

Instruction

Lectures, problem classes, laboratory work, compulsory assignments.

Assessment

Written examination at the end of the course and compulsory assignments.

Reading list

Reading list

Applies from: week 27, 2007

Some titles may be available electronically through the University library.