Computational Methods in Modern Physics

Learning Outcomes

  1. Describe the curved spacetime and light trajectories in the curved spacetime.
  2. Apply ray tracing technique to optics problems.
  3. Describe the crystal lattice and interatomic forces.
  4. Apply the concept of force and energy to finding the optimum configuration of a system.
  5. Explain the concept of short and long range interactions.
  6. Identify quantitatively abrupt structural change in a system.
  7. Apply a machine learning algorithm to a classification problem.

Forms of Teaching

Lectures

Exercises

Independent assignments

Week by Week Schedule

  1. High-energy physics events.
  2. High-energy physics events; Application of machine learning to event classification.
  3. Application of machine learning to event classification.
  4. Metric in curved spacetime.
  5. Geodesic equation; Metric in curved spacetime.
  6. Geodesic equation; Tracing photon trajectories.
  7. Tracing photon trajectories.
  8. Midterm exam.
  9. Van der Waals force interactions.
  10. Van der Waals force interactions; Graphene layers.
  11. Graphene layers.
  12. Percolation concepts; Abrupt transitions in behavior; Long range connectivity.
  13. Percolation concepts; Abrupt transitions in behavior; Long range connectivity.
  14. Electrical conductivity in composite materials.
  15. Final exam.

Study Programmes

University undergraduate
Computing (study)
Elective Courses (6. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (6. semester)
University graduate
Audio Technologies and Electroacoustics (profile)
Elective Courses (2. semester)
Communication and Space Technologies (profile)
Elective Courses (2. semester)
Computational Modelling in Engineering (profile)
Elective Courses (2. semester)
Computer Engineering (profile)
Elective Courses (2. semester)
Computer Science (profile)
Elective Courses (2. semester)
Control Systems and Robotics (profile)
Elective Courses (2. semester)
Data Science (profile)
Elective Courses (2. semester)
Electrical Power Engineering (profile)
Elective course (2. semester)
Electric Machines, Drives and Automation (profile)
Elective Courses (2. semester)
Electronic and Computer Engineering (profile)
Elective Courses (2. semester)
Electronics (profile)
Elective Courses (2. semester)
Information and Communication Engineering (profile)
Elective courses (2. semester)
Network Science (profile)
Elective Courses (2. semester)
Software Engineering and Information Systems (profile)
Elective Courses (2. semester)

Literature

(.), V. Šips, I. Rendulić: Uvod u fiziku čvrstog stanja,
(.), General Relativity, MIT OpenCourseWare https://ocw.mit.edu/ans15436/ZipForEndUsers/8/8-962-spring-2006/8-962-spring-2006.zip,
(.), Modelling Environmental Complexity, Percolation Theory chapter, MIT OpenCourseWare https://ocw.mit.edu/courses/earth-atmospheric-and-planetary-sciences/12-086-modeling-environmental-complexity-fall-2014/lecture-notes/MIT12_086F14_percolation.pdf,
(.), Albert, J., et al. (2008). Implementation of the random forest method for the imaging atmospheric Cherenkov telescope MAGIC. Nuclear Instruments and Methods in Physics Research A, 588, 424,

Lecturers

Asst. Prof.
Ana Babić

For students

General

ID 183467
  Summer semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures
0 Exercises
13 Laboratory exercises
0 Project laboratory