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: 2018-02-27
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2018
  • 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 the properties in algorithms and mathematical models and, based on the evaluation discuss the suitability of these methods given an application problem;
  • solve problems in science and engineering by breaking the problem into sub-problems, use software efficiently and write code using a good programming standard;
  • present, explain, summarise, evaluate and discuss solution methods and results in a report.

Content

Programming in MATLAB and methodology for problem solving. Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods. Order of a method, order of accuracy. Monte Carlo methods and methods based on random number, stochastic models, stochastic simulation, stochastoc ordinary differential equations, inverse transform sampling.

Key concepts covered in the course: discretisation and discretisation error (truncation error), accuracy and order of accuracy, local and global error, efficiency, stability and instabilty, adaptivity, stiff and non-stiff ordinary differential equations, deterministic/stochastic models and methods.

Instruction

Lectures, problem classes, laboratory work, mini projects.

Assessment

Written examination (3 credits) and written report on mini project (2 credits).

Syllabus Revisions

Reading list

Reading list

Applies from: Spring 2018

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

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