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, 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 biomechanical
modelling of humans such as computing the stress field of bones or
computational fluid dynamics solving for the motion of liquids,
gasses, and thin films. Dealing with the 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 that 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.
This course aims to create an overview of typically used simulation methods and techniques. The course seeks to give insight into the application of methods and techniques on examples such as the 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 seeks 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 at a theoretical level but also at implementation level with a 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 implementations to case-study problems like computing the motion of 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 the finite element method (FEM) on a PDE
- Apply the finite volume method (FVM) on a PDE
- Apply the 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.
Academic qualifications equivalent to a BSc degree is recommended.
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.
- Project work
There will be written individual feedback on hands-ins. Oral feedback consists of plenum collectively feedback discussions about common trends and mistakes in hands-ins. The flipped classroom offers students many possibilities for their initiative to discuss their learning progress and learning challenges with teachers as a continuous 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
The re-exam consists of two parts:
1. Resubmission of uncompleted and/or not passed written assignments. The assignments must be submitted no later than two weeks before the date of the oral examination.
2. A 15 minutes oral examination without preparation
The final grade is based on an overall assessment.
Criteria for exam assesment
To obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills, and competences described under Learning Outcome.