NDAK12006U Computational Methods in Simulation (CMIS)
MSc Programme in Computer Science
MSc Programme in Physics
Computational methods in simulation is an important computer
tool in many disciplines like bioinformatics, eScience, scientific
computing and computational physics, computational chemistry,
computational biology, computer animation and many more. A wide
range of problems can be solved using computational methods like:
bio-mechanical modeling of humans such as computing the stress
field of bones or computational fluid dynamics solving for motion
of liquids, gasses and thin films. Dealing with motion of atoms and
molecules using molecular dynamics. Computing the dynamic motion of
Robots or mechanical systems and many more.
This course will build up a toolbox of simulation methods which the student can use when building solutions in his or her future studies. Therefore this course is an ideal supplement for students coming from many different fields in science.
The aim of this course is to create an overview of typically used simulation methods and techniques. The course seek to give insight into the application of methods and techniques on examples such as motion of deformable models, fluid flows, heat diffusion etc. During the course the student will be presented with mathematical models such as a system of partial differential equations. The course seek to teach the student the classical approaches to reformulate and approximate mathematical models in such a way that they can be used for computations on a computer.
This course teaches the basic theory of simulation methods. The focus is on deep learning of how the methods covered during the course works. Both on a theoretical level but also on an implementation level with focus on computer science and good programming practice.
There will be weekly programming exercises where students will implement the algorithms and methods introduced from theory and apply their own implementations to case-study problems like computing the motion of a gas or granular material.
The course will cover topics such as finite difference approximations (FDM), finite volume method (FVM) and finite element method (FEM) etc.
- Computer Simulation
- Theory of discretization methods (FEM, FVM, FDM etc)
- Apply finite element method (FEM) on a PDE
- Apply finite volume method (FVM) on a PDE
- Apply finite difference method (FDM) on a PDE
- Apply a discretization method to a given partial differential equation (PDE) to derive a computer simulation model
- Implement a computer simulator using a high level programming language
See Absalon when the course is set up.
Theorems like fundamental theorem of calculus, mean value theorem or Taylors theorem will be used during the course. The inquisitive students may find more in depth knowledge from Chapters 2, 3, 5, 6 and 13 of R. A. Adams, Calculus, 3rd ed. Addison Wesley.
There will be written individual feedback on hands-ins. Oral feedback consist of plenum collectively feedback discussions about common trends and mistakes in hands-ins. Flipped class room offers students many possibilities for on their own initiative to discuss their learning progress and learning challenges with teachers as a continous feedback option.
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous assessment based on 7-8 written assignments weighted equally.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Resubmission of uncompleted written assignments and a 15 minute oral presentation without preparation. The assignments must be submitted no later than two weeks before the date of the re-exam.
The part-examinations/assignments must be individually approved. The final grade is based on an overall assesment.
Criteria for exam assesment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
- Project work