NDAK12006U Computational Methods in Simulation (CMIS)
MSc Programme in Computer Science
MSc Programme in Physics
Computational methods in simulation are 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 modeling 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 and also at the
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.
Knowledge of
- Computer Simulation
- Theory of discretization methods (FEM, FVM, FDM, etc)
Skills to
- 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
Competences to
- 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.
- Category
- Hours
- Lectures
- 21
- Preparation
- 36
- Exercises
- 49
- Project work
- 100
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutes (no preparation)
- Type of assessment details
- The exam takes an outset in theory taught over the course. Students should be able to derive theory/math on the blackboard during the examination
- Exam registration requirements
To qualify for the exam the student must complete 3 out of a maximum of 4 short reports which can be made as a group or as individual reports. The written reports should be maximally 10 pages.
- Aid
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
The reexam format and qualification for reexam are the same as for the regular exam. The reports must be submitted no later than two weeks before the re-exam week to qualify for the re-exam.
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.
Course information
- Language
- English
- Course code
- NDAK12006U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- C
- Course capacity
- 40
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Melanie Ganz-Benjaminsen (4-76707d894f73783d7a843d737a)