| Course title | System Simulation |
|---|---|
| Course code | KI/ESYS |
| Organizational form of instruction | Lecture + Lesson |
| Level of course | Master |
| Year of study | not specified |
| Semester | Winter |
| Number of ECTS credits | 6 |
| Language of instruction | English |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Course availability | The course is available to visiting students |
| Lecturer(s) |
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| Course content |
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1. Introduction to computer simulations (types of simulations, simulation objectives, areas of application, modern trends) with emphasis on stochastic simulations and continuous and particle modelling of dynamic systems 2. Discrete event-driven simulation in SimPy (principles, basic object types, implementation using coroutines), practical simulation design, implementation and visualization 3. Commercial systems for discrete event simulation and visualization (Simio, SIMUL8, etc.) including real-time 3D simulation systems 4. Dynamical systems (their description, coupling, equilibrium and transient state, linear response of the system, etc.), basic methods for modelling the evolution of dynamical systems (systems of ordinary differential equations) 5. SimuPy, a Python-based tool for continuous modelling of dynamical systems (object definition, object constraints, solution methods, visualisation of results) 6. Practical solution of dynamical systems using SimuPy: physical or biological models 7. Dynamic modeling of particle systems, system descriptions (scales), methods and modern trends, parallelization, simulation packages and trajectory analysis using Python (e.g. LAMMPS, MDAnalysis, etc.) 8. Deterministic and stochastic modeling of simple and complex particle systems (computer models, methods, visualization) 9. Practical problem solving for modeling deterministic and stochastic particle systems
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
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The course is aimed at computer simulations used in a wide range of areas from economic and transport problems to simulations of biological or chemical processes. The lectures are focused on the presentation of existing tools including demonstrations of their use. Simple models of dynamical systems are designed and implemented on appropriate simulation platforms, and the results of the simulations are visualized and analyzed.
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| Prerequisites |
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programming (Python)
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| Assessment methods and criteria |
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unspecified
seminar work (implementation of a specified model and the visualization of the simulation results) + oral examination focusing on both practical and theoretical aspects of simulations |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
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