Modellbildung und Simulation - Summer 17: Difference between revisions
(modsim 17 - 2. Version) |
(modsim 17 - englisch) |
||
| Line 24: | Line 24: | ||
= | = Current Issues = | ||
<!--* '''<span style="color:red">Exam Review on 16.08.2015 between 15:00 hrs und 16:30 hrs in MI 02.05.036</span>''' | <!--* '''<span style="color:red">Exam Review on 16.08.2015 between 15:00 hrs und 16:30 hrs in MI 02.05.036</span>''' | ||
* Die Fragestunde findet am 28.7 zwischen 14 Uhr und 15:30 Uhr im HS 2 statt. | * Die Fragestunde findet am 28.7 zwischen 14 Uhr und 15:30 Uhr im HS 2 statt. | ||
| Line 38: | Line 38: | ||
= | = Topic = | ||
Models are simplifying abstract representations of real systems, simulations are (mostly/always) computer-aided experiments, based upon a model. To understand, predict and optimize the behavior of a system more efficient and expressive simulations are necessary. Corresponding to the immense variety of modelling, as well as to-be-modeled systems (e.g. climate, weather, chemical or biological reactions, crash-tests, stock prices, scheduling, traffic, traffic in computing systems, software systems) a wide range of fundamentally different mathematical and informatical methods is used. Those can be deterministic or stochastic, discrete or numeric, but also less formal methods, as textual or graphic descriptions (e.g diagrams). Nevertheless there exist various general principles, e.g. for the derivation, analysis or evaluation of a model. | |||
In | In this lecture an introduction to mathematical-informatical modeling is given. Hereby various topics are discussed. This includes model classes, the choice of proper instruments to formally describe a model, the derivation of models, as well as properties of models. | ||
Multiple examples of discrete models and simulation methods (Decision theory, scheduling, discrete event simulation), as well as examples of continuous models and their simulation techniques (population dynamics, control theory, traffic simulations, heat conduction) from a wide range of scientific backgrounds are presented. Hereby the necessary tools, the derivation of the model, and its realization in a simulation are elaborated. | |||
This lecture shines light on these topics in the context of computer science. The necessary mathematical contents are discussed; this course does not require the students to have any prior knowledge that exceed the undergraduate level. | |||
= | = Literature = | ||
* | * A fitting book for the lecture (English version available): [[Buch Modellbildung und Simulation|Modellbildung und Simulation - Eine anwendungsorientierte Einführung]] | ||
: [[Image:ModSimBuch_Cover.jpg]] | : [[Image:ModSimBuch_Cover.jpg]] | ||
[[Category:Teaching]] | [[Category:Teaching]] | ||
Revision as of 12:25, 1 December 2016
- Term
- Summer 17
- Lecturer
- Univ.-Prof. Dr. Hans-Joachim Bungartz
- Time and Place
- Audience
- Modul IN2010
- Informatics Diplom: Elective course in the field of theoretical computer science
- Informatics Bachelor: Elective course
- Information Systems(Wirtschaftsinformatik) Bachelor: Elective course
- Informatics Master: Elective course in the Area of "Algorithms and Scientific Computing"
- Computational Science and Engineering: Elective course (Application Catalogue E1)
- Students of Mathematics, Science and Engineering
- Tutorials
- Paul Sarbu, Steffen Seckler
- Time:
- Exam
- Semesterwochenstunden / ECTS Credits
- 6 SWS (4V + 2Ü) / 8 Credits
- TUMonline
- Lecture, Tutorial,
Current Issues
Exam Instructions
- You may only use one hand-written sheet of paper (size A4, on both pages).
- Any other material including electronic devices of any kind is forbidden.
- Do not use pencil, or red or green ink.
Topic
Models are simplifying abstract representations of real systems, simulations are (mostly/always) computer-aided experiments, based upon a model. To understand, predict and optimize the behavior of a system more efficient and expressive simulations are necessary. Corresponding to the immense variety of modelling, as well as to-be-modeled systems (e.g. climate, weather, chemical or biological reactions, crash-tests, stock prices, scheduling, traffic, traffic in computing systems, software systems) a wide range of fundamentally different mathematical and informatical methods is used. Those can be deterministic or stochastic, discrete or numeric, but also less formal methods, as textual or graphic descriptions (e.g diagrams). Nevertheless there exist various general principles, e.g. for the derivation, analysis or evaluation of a model.
In this lecture an introduction to mathematical-informatical modeling is given. Hereby various topics are discussed. This includes model classes, the choice of proper instruments to formally describe a model, the derivation of models, as well as properties of models.
Multiple examples of discrete models and simulation methods (Decision theory, scheduling, discrete event simulation), as well as examples of continuous models and their simulation techniques (population dynamics, control theory, traffic simulations, heat conduction) from a wide range of scientific backgrounds are presented. Hereby the necessary tools, the derivation of the model, and its realization in a simulation are elaborated.
This lecture shines light on these topics in the context of computer science. The necessary mathematical contents are discussed; this course does not require the students to have any prior knowledge that exceed the undergraduate level.
Literature
- A fitting book for the lecture (English version available): Modellbildung und Simulation - Eine anwendungsorientierte Einführung
