Wintersemester 2026/2027
Willkommen zur Lehr-veranstaltung
Simulation and Modeling I
Die Vorlesung startet mit einer Einführung in das Feld der Simulation. Vermittelt werden u.a. Inhalte zur diskreten Modellierung, Eingabe-Modellierung, Zufallszahlen-Generierung, Ausgabe-Analyse.
Außerdem Modellierungs-Paradigmen, wie z.B. Ereignis-/Prozess-orientiert oder Petri-Netz. Simulation und AI runden die Vorlesung ab. Praktische Übungen bieten erste Erfahrungen mit Modellierung und verschiedenen Simulationswerkzeugen.
Details (vorläufig)
| Veranstaltung | Vorlesung (2 SWS) + Übungen (2 SWS) |
| ECTS Credits | 2,5 + 2,5 |
| Sprache | English |
| Vorlesung | Donnerstag, 10:15 – 11:45, Raum 11501.04.023 (04.023 Hörsaal) Dozent: Prof. German |
| Abgabe | Übungen | Montag, 10:00 – 12:00 und 12:00 – 14:00, Raum 0.157-115 CIP Pool EEI campo Erster Termin: voraussichtlich 26. Oktober. |
| Theorie | Unterstützungs-Übungen | Donnerstag, 12:00 – 14:00 und 14:00 – 16:00, Raum 0.157-115 CIP Pool EEI campo Erster Termin: voraussichtlich 22. Oktober |
| Dozenten | Dr. Anna Baron / Jonathan Fellerer, M.Sc. / Timm Bugla |
The lectures, exercises and the materials are completely in English.
Please change to the english version of this site.
In case of any questions, contact sam1@i7.informatik.uni-erlangen.de
Overview of the various kinds of simulation, discrete simulation (computational concepts, simulation of queuing systems, simulation in Java, professional simulation tools), required probability concepts and statistics, modeling paradigms (e.g., event/process-oriented, queuing systems, Petri nets, UML statecharts), input modeling (selecting input probability distributions), random number generation (linear congruential generators and variants, generating random variates), output analysis (warm-up period detection, independent replications, result presentation), continuous and hybrid simulation (differential equations, numerical solution, hybrid statecharts), simulation software, case studies, parallel and distributed simulation.
The exercise is optional. You can earn bonus points for the exam. It will be a mixture of practical simulation tasks (as homework / with support during the exercise) and interactive theoretical exercise sessions.
Contents include: Calculation of expected values with the aid of probability theory, determination of confidence intervals, creation of simulation models using AnyLogic, hybrid modeling with statecharts, data collection and distribution fitting.
The exercises will be held on Mondays and serve for practice of theory, usage of various tools, and for preparation of assignments. It will also have some exercise with interactive solving of theoretical tasks.
When questions arise, please use the message board in StudOn. Personal questions can be asked at sam1@i7.informatik.uni-erlangen.de.
Law, Kelton: „Simulation Modeling and Analysis.“ 5rd edition, McGraw Hill, 2014.
AnyLogic in 3 Days (free tutorial book)
Zuletzt aktualisiert: 2026-09-15 – 15:14