Inhalt

[ 921CGELMOCV13 ] KV (*)Modeling and Computer Simulation

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Herbert Prähofer 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2025W
Lernergebnisse
Kompetenzen
(*)Students have a comprehensive understanding of the key methods and tools used in modeling and computer simulation. They are able to create and apply models to simulate real-world systems, encompassing continuous, discrete, and hybrid systems. Through hands-on experience with simulation tools, students are equipped to analyze complex systems using differential equations, stochastic methods, and agent-based models. They can evaluate and interpret simulation results, using statistical methods to draw conclusions. They are prepared to apply simulation in a wide range of application domains, such as engineering, economics, or biology.
Fertigkeiten Kenntnisse
(*)
  • Develop and implement models based on differential equations (K6)
  • Know how to apply numerical integration for continuous systems (K2, K3)
  • Create and simulate discrete event systems (K3, K6)
  • Conduct stochastic simulations and perform statistical evaluations (K3, K4)
  • Model and simulate hybrid systems combining continuous and discrete processes (K6, K3)
  • Design agent-based simulations for complex systems (K6, K3)
  • Use simulation tools (e.g., AnyLogic or Simulink) to perform various types of simulations (K3)
  • Interpret and evaluate simulation results using statistical methods (K5)
(*)
  • Differential equation models and their use in continuous systems
  • Methods of numerical integration and their application to dynamic systems
  • Discrete event systems and discrete simulation techniques
  • Stochastic simulation methods and the use of statistical tools for evaluation
  • Hybrid systems combining both continuous and discrete components
  • Agent-based simulation for modeling individual behaviors within complex systems
  • Various application domains for modeling and simulation, such as engineering, biology, and social sciences
  • Using simulation tools in different area of application
Beurteilungskriterien (*)Student projects, oral exam
Lehrmethoden (*)Slide-based lecture, demonstration of applications and tools, student projects
Abhaltungssprache Englisch
Literatur (*)Lecture slides
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Präsenzlehrveranstaltung
Teilungsziffer -
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