Lucas Böttcher: Catalogue data in Spring Semester 2019 |
Name | Dr. Lucas Böttcher |
URL | http://lucas-boettcher.info/ |
Department | Physics |
Relationship | Lecturer |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
402-0812-00L | Computational Statistical Physics | 8 credits | 2V + 2U | L. Böttcher | |
Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | ||||
Objective | The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements. | ||||
Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | ||||
Lecture notes | Lecture notes and slides are available online and will be distributed if desired. | ||||
Literature | Literature recommendations and references are included in the lecture notes. | ||||
Prerequisites / Notice | Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended. |