| Title | Time | Room | Instructor |
|---|---|---|---|
| Monte Carlo Methods in Statistical Physics | 10.10.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 12.10.2022 13:15 - 14:30 (Wed) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 17.10.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 19.10.2022 13:15 - 14:30 (Wed) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 24.10.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 31.10.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 02.11.2022 13:15 - 14:30 (Wed) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 07.11.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 09.11.2022 13:15 - 14:30 (Wed) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 14.11.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 16.11.2022 13:15 - 14:30 (Wed) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 21.11.2022 13:15 - 14:30 (Mon) | Goodrich, Carl | |
| Monte Carlo Methods in Statistical Physics | 23.11.2022 13:15 - 14:30 (Wed) | Goodrich, Carl |
Description:
This course will teach basic concepts of statistical physics through hands-on Monte Carlo simulations of the Ising model and related systems. The goal is for students to gain intuition for the physics near critical points while developing essential computational skills. Numerical concepts will be discussed in lectures, but students will be expected to write, run, and analyze their own simulations.
Capacity:
8/20
Course Code:
C_PHY-3001_F22
Course instructor(s):
Carl Goodrich
Course type:
Taught course
Course level:
Advanced/foundational
Primary Track:
Physics
Secondary Track(s):
Chemistry & Materials
Computer Science
Data Science & Scientific Computing
Course format:
On campus
Duration:
Half semester
ECTS:
3
Semester:
Fall 1
Minimum number of participants:
4
Target audience:
Physics students with desire to learn numerical techniques, DSSC students with some background or interest in phsyics.
Prerequisites:
Basic knowledge of a modern programming language (recommended C++, Python, or Julia). Background in statistical mechanics is encouraged.
Teaching format:
lectures
Assessment form(s):
regular assignments, participation
Grading scheme:
Pass/fail
Course Category:
Credit Course
Academic Year:
AY 2022/23