Last modified on January 11, 2021
Messages from Instructor:
Class Hour: Tue/Thu 11:00 - 12:15
Course Format: Remote
All lectures are given online via ZOOM.
Course Description:
Through this course students will learn various numerical methods apply them to a variety of physics problems in classical mechanics, electromagnetism, quantum mechanics and statistical mechanics, using MATLAB or Python. Students successfully completing this course will develop sufficient computational skills necessary to investigate basic physics problems numerically or to carry out simple computer simulations.
Learning Objectives:
Numerical algorithms to solve the follwoing methematical problems.
MATLAB
MATLAB is used in all lectures. The use of MATLAB is required in this course. UAB has a site license and students are elligible to install MATLAB on their computer. Installation instruction is given at https://docs.uabgrid.uab.edu/wiki/Matlab_site_license. Several tutorials are availble within MATLAB.
Python
Python is another popular computer language. Although we use MATLAB as main language in the lectures, Phython 3 is also supported. If you wish to use Python, Anadonda package is recommended for Microsoft Windows and Mac. (Linux is also supported by Anaconda but it is better to use the python package included in yourLinux distros.) The instructor uses Python 3 (Not Python 2). Download Anaconda for Python 3.7 at https://www.anaconda.com/download/. Every Linux distribution inlucdes Python 3. Use a software installer in your Linux system. If you are new to python, read this webpages Getting started with Python for science.
Other Computer Languages
You can use your favorite computer language to solve homework and project problems. However, the instructor uses only MATLAB and Python in this course. It is not intended to teach other computer languages in class. Students are responsible for learning the language by themselves.
Homework
You are required to write at least one short computer program every week. You pick one problem given in the lecture note. Homework must be turned in electronically by email. Allowed formats are MATLAB script file, Python script file, or source codes in other languages such as java, c++, fortran, etc.
Projects
There is no paper exam. Students must submit two projects, one by March 11 and the other by April 27. See the detailed project schedule here. Students can work on the porjects suggested by the instructors or propose their own projects. The graduate students are encourage to work on a project related to their research field. The project must use sufficientl level off computational methods. The instructor suggests appropriate level of computational methods for the proposed project. In general graduate project for PH762 must include higher level of computational methods than undergraduate project for PH423. The instructor will guide each project individually to make it sure that the project contains sufficient level of computational methods.
Attendance
Attendance is required. To pass the course you must attend at least 75% of lectures. Excessive absence will result in administrative withdraw.
Grading
Each project carries 30 pts and homework 40 pts. The total maximum possible points is 100 pts. You must complete two projects and 75% of homework or otherwise receive F regardless of the net scores. Letter grades are determined by the rule given in the table.
Grade | Total Score |
---|---|
A | 90 or above |
B | 80 or above |
C | 70 or above |
F | Otherwise |
Where the university can, it is providing a Pass/Fail option in case there are circumstances and/or challenges students are encountering related to the ongoing pandemic that might make a Pass/Fail option a better option. If students are not remaining with the default letter grade method for any of their courses, they must select the Pass/Fail grading method for each course individually. This selection is made toward the end of the semester. Once a student selects the option for a Pass/Fail grading method for a particular course, that decision is not reversible regardless of their performance on remaining assignments or final exams.
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