Curriculum.

Course title Computational Physics
Code F133
Status Lectures(15), Seminars (30), Exercises (15)
Level Required course
Year 3. Semester 6.
ECTS 5
Lecturer Dario Hrupec, Assistant Professor
Igor Miklavčić, Lecturer
Course objective Numerically solve and graph physical problems using a computer.
Prerequisites Computer Laboratory (code I116)
Learning outcomes: After successfully completed course, student will be able to

  1. Apply Python to numerically solve physics problems.
  2. Apply Monte Carlo simulations.
  3. Apply stochastic methods.
  4. Numerically solve systems of nonlinear equations.
  5. Numerically solve ordinary differential equations.
  6. Numerically solve multiple integrals.
  7. Use numerical methods for curve fitting.
  8. Use Python’s numeric modules ScyPy and NumPy.
  9. Use Python’s graphics module MatPlotLib.
  10. Use the Linux operating system.
Teaching activity ECTS Learning outcome Students’ activity Methods of evaluation Points
min max
Class attendance 1 1-10 Attends classes Evidence list 0 15
Independent exercises of program codes writing 2 1-10 Solves weekly individual exercises Weekly scoring of independent work 0 45
Final exam 2 1-10 Modifies a program code in order to get required new functionality Oral exam 0 40
Total 5 0 100
Consultations by appointment
Gained competencies GENERAL COMPETENCIES:

  1. Application of computers for numerical analysis of physical problems and graphing of their solutions
  2. Computer applications for stochastic process simulations and Monte Carlo simulations
  3. Writing own program code for experimental or observational data analysis
  4. Advanced level of Python programming
  5. Basic level of Linux using

SPECIFIC COMPETENCIES:

  1. problem solving
  2. logical thinking
Course curriculum
  • Python basics
  • Strings, lists, arrays, and dictionaries
  • Input and output
  • Plotting
  • Conditionals and loops
  • Functions
  • Basic numerical tools
  • Numerical Routines: SciPy and NumPy
  • Numpy, Scipy, and MatPlotLib
  • Monte Carlo techniques
  • Stochastic methods
  • Curve fitting
  • Ordinary differential equations
  • Chaos
Recommended reading David Pine, Introduction to Python for Science and Engineering, CRC Press, 2019.
Eric Ayars, Computational Physics with Python, 2013.
Additional reading Ben Stephenson, The Python Workbook: A Brief Introduction with Exercises and Solutions, 2nd Edition, Springer, 2019.
Zvonko Glumac, Računalne metode fizike: kratak uvod, 2015.
Instructional methods One hour of lectures, two hours of seminars and one hour of exercises per week. Students actively participate in classes so that, each on their own computer, writes, tests and corrects their own program code for a given problem.
Exam formats Every week, a student get an individual excercise that needs to be solved independently and which is graded. At the final oral exam, a student modifies given program codes in order to get a new program functionality.
Language Croatian
Quality control and successfulness follow up Student survey
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