Statistical Physics 0800-FISTAT

http://www.fizyka.umk.pl/~jacekj/FS.htm

Lectures
1. Basics of probability theory.
2. Stochastic processes, Markov chains and Langevin equation.
3. Entropy vs. information. States of maximal entropy
4. Description of statistical systems. Evolution and equilibrium states.
Thermodynamic formalisms.
5. Thermodynamics of gas systems:
a) perfect gas
b) nonideal gases (virial expansion, mean field theory)
6. Thermodynamics of magnetic systems:
a) paramagnetics and Curie law
b) Ising model of nearest neighbours
c) phase transition in a Curie-Weis-Kac model
7. Grand canonical ensemble and theory of phase transitions
8. Quantum Statistical systems:
a) formalism of quantum mechanics
b) multilevel system: Bose-Einstein and Fermi-Dirac statistics
9. Quantum gases
a) electron gas in metal
b) stability of white dwarfs
c) Bose-Einstein condensation
d) photonic gas and thermal radiation
e) phonons and crystals
f) basics of superconductivity

Exercise: selected topics of statistical physics

Term 2022/23Z:

None

Term 2024/25Z:

None

Term 2025/26Z:

None

Total student workload

45 h lectures + 45 h exercises + 87 h homework + 3 h exam

Learning outcomes - knowledge

K_W01: The graduate has basic knowledge of physics corresponding to the second cycle programme level as well as advanced knowledge of statistical physics K_W02: The graduate has in-depth knowledge of advanced maths and mathematical methods, necessary for solving physics-related problems in statistical physics K_W05: The graduate has in-depth knowledge of statistical physics, thermodynamics of gaseous and magnetic systems, and quantum statistical physics K_W06: The graduate has knowledge concerning the current trends in the development of physics, particularly in statistical physics

Learning outcomes - skills

K_U01: The graduate is able to use scientific methods in problem-solving, conducting experiments and conclusive reasoning in statistical physics K_U05: The graduate is able to adapt knowledge and methodology of statistical physics as well as theoretical and experimental methods to astronomy and/or biophysics

Learning outcomes - social competencies

K-K01: The graduate knows the level of his or her knowledge and skills and is able to formulate questions adequately. The graduate understands the need for further education.

Teaching methods

lecture+ exercise

Expository teaching methods

- informative (conventional) lecture

Exploratory teaching methods

- practical
- classic problem-solving

Type of course

compulsory course

Prerequisites

Basic knowledge of notions and methods of classical mechanics, quantum physics, mathematical analysis and linear algebra

Course coordinators

Jacek Jurkowski

Assessment criteria

Written exam + oral discussion

50-60% - grade: 3
60-70% - grade: 3+
70-80% - grade: 4
80-90% - grade: 4+
90-100% - grade 5

Bibliography

- R. S. Ingarden, Termodynamika statystyczna,
- J. Łopuszański, A. Pawlikowski, Fizyka statystyczna,
- A. I. Anselm, Podstawy fizyki statystycznej i termodynamiki,
- K. Huang, Podstawy fizyki statystycznej,
- K. Huang, Mechanika statystyczna,
- R. S. Ingarden, A. Jamiołkowski, R. Mrugała, Fizyka statystyczna i termodynamika,
- A. K. Wróblewski, J. A. Zakrzewski, Wstęp do fizyki, tom 2, część 2.
- C. J. Thompson, Mathematical Statistical Physics
- R. Balescu, Equilibrium and Nonequilibrium Statistical Mechanics
T. C. Dorlas, Statistical Mechanics. Fundamentals and Model Solutions
H.-P. Brewuer, F. Petruccione, The Theory of Open Quantum Systems

Term 2022/23Z:

None

Term 2024/25Z:

None

Term 2025/26Z:

None

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 0800-FISTAT in USOSweb