Marina Krstic Marinkovic: Catalogue data in Spring Semester 2021

Name Prof. Dr. Marina Krstic Marinkovic
Name variantsMarina Marinkovic
Marina Krstic Marinkovic
FieldComputational Physics
Address
Institut für Theoretische Physik
ETH Zürich, HIT G 33.2
Wolfgang-Pauli-Str. 27
8093 Zürich
SWITZERLAND
Telephone+41 44 633 82 33
E-mailmarinama@ethz.ch
DepartmentPhysics
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
402-0812-00LComputational Statistical Physics Information 8 credits2V + 2UM. Krstic Marinkovic
AbstractComputer 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.
ObjectiveThe 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.
ContentComputer 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 notesLecture notes and slides are available online and will be distributed if desired.
LiteratureLiterature recommendations and references are included in the lecture notes.
Prerequisites / NoticeSome basic knowledge about statistical physics, classical mechanics and computational methods is recommended.