Objectives and competences
- Depict the role of modeling and simulation in today's science at the complex systems analysis
- Teach the students the methods and techniques of modeling and simulation in the sense of hybrid models
- Determination of complex simulation models application in the form of dynamical simulators
- Present the field of model application, effectiveness and facets at the support of business decisions
- Study of the different methods of validation and verification of complex models and interpretation of results
- Study of nonlinear systems in the field of organizational sciences
Content (Syllabus outline)
- Complex system simulation paradigm
- Classification of mathematical and simulation models
- Application fields of continuous, discrete event and agent-based models
- Qualitative and quantitative modeling of continuous systems: directed graphs, system dynamics, nonlinear chaotic systems, sensitivity analysis
- Discrete event simulation
- Random generators and statistical distributions
- Modeling with event graphs
- Process oriented models
- Modeling with Petri nets
- Agent based models
- Modeling of Cyber-physical systems
- Hybrid models
- Validation of models
- Experimental design and result analysis
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• Ability to define the specific problem as the simulation model by the application of hybrid simulation techniques
• Analysis of data and design of experiments
• Knowledge and understanding of simulation tools and their application
• Understanding of different simulation techniques and areas of application
• Mastering of holistic development of systems for decision support based on the hybrid simulation techniques
Intended learning outcomes - transferable/key skills and other attributes
- Foster interdisciplinary collaboration between academia, research and industry experts at the problem solving with the methods of simulation
- Mastering of holistic development of systems for decision support based on the hybrid simulation techniques
Readings
1. Strogatz, S. (20152018, cop.). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering (2nd ed., str. XIII, 513 , 4 barvnih pril.). CRC Press, Taylor & Francis Group.
2. Gros, C. (2009) Complex and Adaptive Dynamical Systems: A primer. Springer, New York.
3. Severance, F. L. (2001) System Modeling and Simulation: An Introduction, John Wiley & Sons, Chichester.
4. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems (2nd ed., str. XXI, 510). Academic Press.
5. Downey, A. (2023). Modeling and simulation in Python: an introduction for scientists and engineers (str. XXVI, 248). No Starch Press.
6. Li, R., & Nakano, A. (2022). Simulation with python: develop simulationand modeling in natural science, engineering, and social sciences (str. XV, 166). Apress.
