Computational Methods in Simulation (CMIS)
Course content
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:
biomechanical 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 casestudy 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.
MSc Programme in Computer Science
MSc Programme in Physics
Knowledge
 Computer Simulation
 Theory of discretization methods (FEM, FVM, FDM etc)
Skills
 Apply finite element method (FEM) on a PDE
 Apply finite volume method (FVM) on a PDE
 Apply finite difference method (FDM) on a PDE
Competences
 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
Mixture of lectures, study groups and project group work with handins.
See Absalon when the course is set up.
It is expected that students know how to install and use Matlab
by themselves. It is also expected that students know what matrices
and vectors are and that students are able to differentiate vector
functions.
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 handsins. Oral feedback consist of plenum collectively feedback discussions about common trends and mistakes in handsins. 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.
As
an exchange, guest and credit student  click here!
Continuing Education  click here!
PhD’s can register for MSccourse by following the same procedure as creditstudents, see link above.
 ECTS
 7,5 ECTS
 Type of assessment

Continuous assessmentContinuous assessment based on 78 written assignments weighted equally.
 Aid
 All aids allowed
 Marking scale
 7point grading scale
 Censorship form
 No external censorship
Several internal examiners
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Single subject courses (day)
 Category
 Hours
 Lectures
 21
 Preparation
 36
 Exercises
 49
 Project work
 100
 English
 206
Kursusinformation
 Language
 English
 Course number
 NDAK12006U
 ECTS
 7,5 ECTS
 Programme level
 Full Degree Master
 Duration

1 block
 Schedulegroup

C
 Capacity
 No limit
 Studyboard
 Study Board of Mathematics and Computer Science
 Department of Computer Science
Course Coordinator
 Kenny Erleben (56f6972727d44686d326f7932686f)
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Kursusinformation for indskrevne studerende