SylabUZ
| Course name | Introduction to computer simulations |
| Course ID | 13.2-WF-FizP-ICS-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | First-cycle studies leading to Bachelor's degree |
| Beginning semester | winter term 2020/2021 |
| Semester | 6 |
| ECTS credits to win | 7 |
| Available in specialities | Computer Physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
|
| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Credit with grade |
| Laboratory | 45 | 3 | - | - | Credit with grade |
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.
| Outcome description | Outcome symbols | Methods of verification | The class form |
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.
Modified by dr hab. Piotr Lubiński, prof. UZ (last modification: 04-06-2020 15:13)
