Modelling and Simulation
Module IN2010
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2012 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |
|---|---|
| SS 2012 | WS 2011/2 |
Basic Information
IN2010 is a semester module in English language at Bachelor’s level and Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 240 h | 90 h | 8 CP |
Content, Learning Outcome and Preconditions
Content
- Introduction to mathematical modelling (notions and notations, fields of application, derivation of models, analysis of models, classification of models, scales and hierarchy)
- Discrete modelling and simulation (decision models: games, strategies, elections; scheduling problems; discrete event simulation: data and job traffic; neural networks)
- Continuous modelling and simulation (population dynamics: models and their numerical treatment; control: deterministic and fuzzy logic approaches; traffic flow: modelling via continuous quantities; heat conduction: models and their numerical treatment)
- Modelling in software design (optional; basic concepts, description techniques, methodology)
- Discrete modelling and simulation (decision models: games, strategies, elections; scheduling problems; discrete event simulation: data and job traffic; neural networks)
- Continuous modelling and simulation (population dynamics: models and their numerical treatment; control: deterministic and fuzzy logic approaches; traffic flow: modelling via continuous quantities; heat conduction: models and their numerical treatment)
- Modelling in software design (optional; basic concepts, description techniques, methodology)
Learning Outcome
The participants are able to develop and to assess formal (mathematical or infomatical) model concepts for a verbally described problem. They can select and apply successfully strategies for simulation, i.e., the computer-aided solution of these models. They know exemplary important model classes and can develop own solution procedures for simple scenarios.
Preconditions
MA0901 Linear Algebra for Informatics, MA0902 Analysis for Informatics, IN0019 Numerical Programming
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 4 | Modelling and Simulation (IN2010) |
Newcome, S.
Reiz, S.
Seitz, P.
Responsible/Coordination: Bungartz, H. |
Tue, 16:00–19:00, Interims I 101 Wed, 10:00–12:00, Interims I 101 |
eLearning |
Learning and Teaching Methods
This module comprises lectures and accompanying tutorials. The contents of the lectures will be taught by talks and presentations.
Students will be encouraged to study literature and to get involved with the topics in depth. In the tutorials, concrete problems will be solved - partially in teamwork - and selected examples will be discussed.
Students will be encouraged to study literature and to get involved with the topics in depth. In the tutorials, concrete problems will be solved - partially in teamwork - and selected examples will be discussed.
Media
Slides, whiteboard, exercise sheets
Literature
- Bungartz, Zimmer, Buchholz, Pflüger: Modellbildung und Simulation - eine anwendungsorientierte Einführung, Springer, 2009
- Fowkes, Mahoney: Einführung in die mathematische Modellierung, Spektrum,1996
- Gander, Hrebicek: Solving Problems in Scientific Computing Using Maple and MATLAB, Springer, 1997
- Bossel: Modellbildung und Simulation, Vieweg, 1994
- Banks et al.: Discrete Event System Simulation, Prentice Hall, 1996
- Golub, Ortega: Scientific Computing: An Introduction with Parallel Computing, Academic Press, 1993
- Nauck, Klawonn, Kruse: Neuronale Netze und Fuzzy-Systeme, Vieweg, 1994
- Fowkes, Mahoney: Einführung in die mathematische Modellierung, Spektrum,1996
- Gander, Hrebicek: Solving Problems in Scientific Computing Using Maple and MATLAB, Springer, 1997
- Bossel: Modellbildung und Simulation, Vieweg, 1994
- Banks et al.: Discrete Event System Simulation, Prentice Hall, 1996
- Golub, Ortega: Scientific Computing: An Introduction with Parallel Computing, Academic Press, 1993
- Nauck, Klawonn, Kruse: Neuronale Netze und Fuzzy-Systeme, Vieweg, 1994
Module Exam
Description of exams and course work
Type of Assessment: exam
The exam takes the form of a 120 minutes written test. In the exam students should prove to be able to identify a given problem and find solutions within limited time. The examination will completely cover the content of the lectures. The answers will require own formulations. In addition, questions requiring short calculations may be posed. Exam questions check the ability to develop and to assess formal (mathematical or informatical) model concepts for a verbally described problem. The assignments test whether the participants are able to select and apply successfully strategies for simulation. Exam tasks evaluate the students' knowledge on important model classes and corresponding solution procedures for simple scenarios.
The exam takes the form of a 120 minutes written test. In the exam students should prove to be able to identify a given problem and find solutions within limited time. The examination will completely cover the content of the lectures. The answers will require own formulations. In addition, questions requiring short calculations may be posed. Exam questions check the ability to develop and to assess formal (mathematical or informatical) model concepts for a verbally described problem. The assignments test whether the participants are able to select and apply successfully strategies for simulation. Exam tasks evaluate the students' knowledge on important model classes and corresponding solution procedures for simple scenarios.
Exam Repetition
The exam may be repeated at the end of the semester.