Computational Physics 2

Course guarantor:

Ondřej Klimo

Lecturer:

Ondřej Klimo

Tutor:

Ondřej Klimo

Supervisor:

Department of Laser Physics and Photonics

Synopsis:

The course is giving an overview of some of the well-known computational physics methods in various fields of physics. The first part concentrates on particle simulation methods - molecular dynamics, Monte Carlo method and other methods of solving the particle transport in self-consistent fields (e.g. Particle in Cell method in plasma physics). The second part concentrates on methods of solving Maxwell equations and in particular on the finite difference and finite elements methods. An introduction to machine learning methods in physics is also given.

Requirements:

Preparation of a brief presentation describing a simulation code. This may be a code used in a research project / masters thesis, or any code for solving a physical problem in general. The presentation should include a description of the physical equations and the numerical solution method. The presentation will be delivered during the last class of the semester.

Syllabus of lectures:

1. Introduction to Molecular dynamics, interaction potentials and solving the equations of motion

2. Measurements in molecular dynamics, equilibrium and dynamical properties of simple fluids, i nitial and boundary conditions, long-range potentials

4. Introduction to Monte Carlo method, Metropolis algorithm

5. Kinetic Monte Carlo simulations for particle transport problems - Monte Carlo solution of transport equation, types of interactions, techniques to reduce the simulation time and the variance of the results

7. Charged particle transport in plasmas using Particle in Cell method

8. Particle in Cell method - equations of motion, interpolation of quantities on the grid, particle shapes, stability and applicability of the method

9. Methods for solving Maxwell equations, overview of the methods and their properties

10. Finite Difference Time Domain method and boundary conditions, Finite Element method

11. Machine learning methods in physics

Syllabus of tutorials:

like lecture

Study Objective:

Knowledge: Theory and implementation of some of the well-known computational physics methods in various fields of physics.

Skills: Use and application of the well-known computational physics methods in various fields of physics.

Study materials:

Key references:

[1] A. Bondeson, T. Rylander, P. Ingelstrom, Computational Electromagnetics (Texts in Applied Mathematics), Second Edition, Springer, 2013

[2] J. Thijssen, Computational Physics, Second Edition, Cambridge University Press, New York, 2007

[3] D.C. Rapaport, The Art of Molecular Dynamics Simulation 2nd Edition, Cambridge University Press; 2 edition, New York, 2004

Recommended references:

[4] A. Haghighat, Monte Carlo Methods for Particle Transport, CRC Press, Boca Raton, 2016

[5] C.K. Birdsall, A.B Langdon, Plasma Physics via Computer Simulation, Taylor & Francis Gropu, New York, 2005

Media and tools:

none

Note:

english version available

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• Fyzika plazmatu a termojaderné fúze (compulsory course in the program)