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
  • Revised: 2013-05-14
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 20, 2013
  • Entry requirements: Scientific Computing I. Mathematical Statistics is recommended.
  • Responsible department: Department of Information Technology

Learning outcomes

To pass, the student should be able to

  • describe and perform tasks in connection to the key concepts covered in the course;
  • explain the idea behind and apply the algorithms covered in the course;
  • explore properties for numerical methods and mathematical models by using the analysis methods covered in the course;
  • evaluate algorithms and mathematical models and discuss the suitability of these methods on a given problem;
  • 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 report.

Content

Continued programming in MATLAB. Continued problem solving methodology. Data analysis: least squares problems with solution based on the normal equations. Interpolation and piecewise interpolation (including cubic splines). Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods. Monte Carlo methods and methods based on random number, stochastic models, stochastic simulation, inverse transform sampling.
Key concepts covered in the course: discretisation and discretisation error (truncation error), accuracy and order of accuracy, the local and global error, efficiency, stability, adaptivity, stiff and non-stiff ordinary differential equations, deterministic/stochastic models and methods, ansatz, interpolation, least squares.

Instruction

Lectures, problem classes, laboratory work, mini projects.

Assessment

Written examination (3 credits) and approved mini projects (2 credits).

Reading list

Reading list

Applies from: week 14, 2013

Some titles may be available electronically through the University library.

  • Chapra, Steven C. Applied numerical methods with MATLAB for engineers and scientists

    3. international ed.: Boston: McGraw-Hill Higher Education, 2012

    Find in the library

    Mandatory