Valid for: 2020/21
Decided by: PLED F/Pi
Date of Decision: 2020-04-01
Language of instruction: The course will be given in English
A core problem in Scientific Computing is the solution of nonlinear and linear systems. These arise in the solution of boundary value problems, stiff ODEs and in optimization. Particular difficulties appear when the systems are large, meaning millions of unknowns. This is often the case when discretizing partial differential equations which model important phenomenas in science and technology. Due to the size of the systems they may only be solved using iterative methods.
The aim of this course is to teach modern methods for the solution of such systems.
The course is a direct follow up of the course FMNN10 Numerical Methods for Differential Equations, and expands the student's toolbox for calculating approximative solutions of partial differential equations.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
be able to decide, given information about a nonlinear or linear system, which solver to use and which not to.
Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Computational project with written report. Take home exam and/or oral exam, to be decided by the examiner at the start of the course.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Assumed prior knowledge: FMNN10 Numerical Methods for Differential Equations.
The number of participants is limited to: No
The course overlaps following course/s: NUMN30, FMNN15
Teacher: Philipp Birken, philipp.birken@na.lu.se
Course coordinator: Anders Holst, studierektor@math.lth.se
Course administrator: Student Office, expedition@math.lth.se
Course homepage: http://www.maths.lu.se/utbildning/numerisk-analys/courses-in-numerical-analysis/