SylabUZ
| Nazwa przedmiotu | Introduction to computer simulations |
| Kod przedmiotu | 13.2-WF-FizP-ICS-S17 |
| Wydział | Wydział Fizyki i Astronomii |
| Kierunek | Fizyka |
| Profil | ogólnoakademicki |
| Rodzaj studiów | pierwszego stopnia z tyt. licencjata |
| Semestr rozpoczęcia | semestr zimowy 2022/2023 |
| Semestr | 6 |
| Liczba punktów ECTS do zdobycia | 7 |
| Występuje w specjalnościach | Fizyka komputerowa |
| Typ przedmiotu | obowiązkowy |
| Język nauczania | angielski |
| Sylabus opracował |
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| Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
| Wykład | 30 | 2 | - | - | Zaliczenie na ocenę |
| Laboratorium | 45 | 3 | - | - | Zaliczenie na ocenę |
The aim of the course is to gain basic knowledge of computer simulations of selected methods for problems of deterministic and Monte Carlo-type issues. Students should acquire skills of implementation of this knowledge by designing an algorithm and a computer program and then interpreting the results of computer simulations. Specific examples will include e.g. problems of molecular dynamics of a single particle, molecular dynamics with constraints, modeling Brownian motion and other random events for different distributions of random variables.
Programming skills in C / C + +, Python or Java and knowledge of numerical methods.
- Representation of numbers, excess and underflow errors, truncation error (finite difference method), the stability of numerical algorithms.
- Algorithms for solving the equation of motion: Euler, Verlet, velocity Verlet, leap-frog predictor-corrector algorithm, the choice of the time step, the stability and accuracy of the algorithms, numerical solution of the harmonic oscillator 1D and 2D.
- Monte Carlo algorithms (random number generators, random variables with different probability distributions, Metropolis algorithm, stochastic equations).
- Cellular automata.
- Genetic algorithms.
Lectures and laboratory exercises, discussions, independent work with a specialized scientific literature in Polish and English, and work with the technical documentation, search for information on the Internet.
| Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Lecture: positive evaluation of the test.
Laboratory: positive evaluation of the tests, the execution of the project.
The final evaluation of the laboratory: evaluation of tests of 60%, the assessment of the project 40%.
Final grade: arithmetic mean of the completion of the lecture and in classes.
[1] J. C. Berendsen and W. F. Van Gunsteren, Practical Algorithms for Dynamic Simulations in Molecular dynamics simulations of statistical mechanical systems, Proceedings of the Enrico Fermi Summer School, p.43-45, Soc. Italinana de Fisica, Bologna 1985.
[2] Stephen Wolfram, Statistical mechanics of cellular automata, Rev. Mod. Phys. 55. 601-644 (1983).
[3] Tao Pang, An Introduction to Computational Physics, Cambridge University Press (2006).
[1] William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery, Numerical recipes, The art of scientific computing, third edition 2007.
Zmodyfikowane przez dr Marcin Kośmider (ostatnia modyfikacja: 04-04-2022 20:51)
