Main field(s) of study and in-depth level:
Computer Science A1N,
Computational Science A1N
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:
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.
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.
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
The Faculty Board of Science and Technology
120 credits including Scientific Computing II, 5 credits, or Scientific Computing, Bridging Course, 5 credits, or Simulation and Numerical Methods, 5 credits. Vector calculus and linear algebra. Proficiency in English equivalent to the Swedish upper secondary course English 6.
On completion of the course, the student should be able to:
explain the idea behind the algorithms that are considered in the course;
account for the fundamental difference between methods based on finite differences and finite elements and their advantages and disadvantages given different application problem;
interpret and relate computational results to the concepts consistency, stability and convergence;
solve problems in science and engineering given a mathematical model, by structuring the problem, choose appropriate numerical method and use advanced software and self-written code to generate solution;
use advanced computational software and interpret results from the computations;
present, explain, summarise, evaluate and discuss solution methods and results and formulate conclusions in a written report
The main focus is on solutions to partial differential equations and methods for solving the resulting equation system. Solution methods based on finite differences and finite element methods. Direct (based on LU-factorization) and iterative methods for solutions to linear systems of equations, and Newtons method for nonlinear systems. Theoretical, practical, implementational as well as validation aspects are discussed in relation to the methods presented in the course. Use of computational software (MATLAB PDE toolbox). Examples of key concepts covered in the course: accuracy and order of accuracy, efficiency, consistency, stability, convergence.
Lectures, problem classes/workouts, laboratory work, compulsory assignments. Guest lecture.
Written examination (3 credits) and approved assignments and tasks at problem solving classes (2 credits). The assignments are reported in English.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.