Spring 2022; Tuesday/Thursday 2:30pm–4:20pm; In-person
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Modern science are heavily based on computational methods and data
analyses, in additional to empirical evidence and scientific theory.
PHYS 105A provides a starting point for students to learn the
essential skills for computation- and data-driven physics.
We will introduce development tools (shell, git, Jupyter, etc),
programming languages (python and C), basic data analysis, and
numerical methods.
This course is prerequisite to PHYS 305 Computational Physics.
None
Please let CK/Gabriele know if plan to come to call into the office hour.
- Instructor: Chi-kwan "CK" Chan
Office Hour: Monday 1--2pm on zoom - Teaching Assistant: Gabriele Bozzola
Office Hour: Wednesday 1--2pm on zoom
Physics Consultation Room (PAS 372) with Gabriele: Thursday 2--3pm. - Course home page: https://github.com/uarizona-2022spring-phys105a/phys105a
Upon completion of this course, students will be able to:
- understand the nature and application of computation methods in physical science;
- use popular development tools (shell,
git, Jupyter, etc); - use good software development practices (version control, documentation, and automation);
- use computational thinking to break down complex physics problems;
- solve these problems by writing programs in the
pythonandCprogramming languages; - speak and write about scientific knowledge;
- appreciate computation complexity and have a basic awareness of numerical errors;
- use data analysis and numerical methods properly, and be aware of their common pitfalls;
- critically analyze and interpret data and results presented in tables, graphs and charts as well as perform appropriate computations;
- read and understand scientific literature from popular sources such as magazines and newspapers;
- aware of a wide range of science use cases, and develop the skill to self-learn computation tools and methods.
The sole goal of this course is to get students ready for modern
computation- and data-driven physics.
In order to do so, we will expose the the students to multiple
development tools and software development best practices.
We will introduce the basic of the python and C programming
languages, and provide hands-on on data analysis and numerical
problems.
We will also introduce basic data analysis methods and numerical
analysis.
After this course, students should be able to solve simple physics
problems numerically, create plots to visualize their results, and
document their work and communicate with other physicists.
See "Expected Learning Outcomes" above.
This course is scheduled to be taught in-person. Session 1 will meet on Tuesday from 2:30pm to 4:20pm, Steward Observatory, Rm 208. Session 2 will meet on Thursday from 2:30pm to 4:20pm, Steward Observatory, Rm 208. Each session will include about 1 hour of lecture and 50 minutes of hands-on exercises. You are required to complete your assignments and projects on time.
If you feel sick, or may have been in contact with someone who is infectious, stay home. Except for seeking medical care, avoid contact with others and do not travel. Notify your instructors if you will be missing a class. Campus Health is testing for COVID-19. Please call (520) 621-9202 before you visit in person. Visit the UArizona COVID-19 page for regular updates.
Email is the official method to communicate with the instructor and teaching assistant.
There is no required text. References will be listed in the lecture notes.
As a course on scientific computing, students are excepted to have
access to a computer.
Students will be asked to install popular development tools such as
git and Jupyter to their computers.
The instructor will provide students additional online videos to broaden the students' knowledge on computational physics. When bundled with assignments, students are required to watch them. When provided as references, the videos are optional.
There will be 8 assignments and 2 projects in total. There will be no quiz nor exam.
The assignment will be assigned approximately once a week. Students are expected to start working on their assignments during the hands-on sessions, and finish them by midnight before the day of the next sessions.
Students are expected to carry out the projects in groups of two or three people. Students will have two weeks to finish their projects (see below).
Although this is not a writing intensive course, good documentation is essential in communicating science and developing software. Students are required to provide enough written explanations in their assignments.
There will be no final exam. Instead, students will need to finish a final group project. The project deadlines are 11:59pm May 2nd for Session 1 and 11:59pm April 27th for Session 2. The two sessions will coordinate a single time slot to present their projects. The final exam schedule will be helpful in selecting such a time.
There are 8 assignments and 2 projects in total. Each assignment worth 10 points and each project worth 20 points.
This course provides regular letter grades (A–E), which are based on a simple point system:
- A: 80–100 points
- B: 70–79.9 points
- C: 60–69.9 points
- D: 50–59.9 points
- E: 0–49.9 points
No scaling will be applied. Nevertheless, the points for a student's worst 20 points worth of assignments or project will not be counted.
Incomplete (I) or Withdrawal (W): Incomplete (I) or withdrawal (W) grades must be made in accordance with University policies, which are available at here and here, respectively.
Dispute of Grade Policy: If a student disagrees on his or her grade on an assignment or project, the student must send the instructor a formal request through email to re-evaluate the grade within a week from the time that the student receives the grade. Because no scale will be applied in the final grade, the final grade cannot be re-evaluated. The student is expected to know of his or her own performance through out the course.
We plan to cover the following topics in PHYS 105A:
| # | Lecture | Hands-on |
|---|---|---|
| 1 | Overview | Sign up for accounts and set up development environment (assignment 5pts) |
| 2 | Essential tools for scientific computing | Unix shells, remote login, version control, compiled vs interpreted languages, etc |
| 3 | The python programming language |
python programming and tests (assignment 5pts) |
| 4 | Random numbers and Monte Carlo methods | Monte Carlo in python (assignment 10pts) |
| 5 | The C programming language |
Monte Carlo in C (assignment 10pts) |
| 6 | Data processing with python |
Jupyter and data processing with python (assignment 10pts); project planning |
| 7 | Numerical integration of functions | Project (project 20pts) |
| 8 | Project presentations | Numerical integrator (assignment 10pts) |
| 9 | Root finding | Root finders (assignment 10pts) |
| 10 | Minimization or maximization | Optimizer (assignment 10pts) |
| 11 | ODE integration. I. | ODE integrator |
| 12 | ODE integration. II. | n-body integrator (assignment 10pts); project planning |
| 13 | Quantum computing | Quantum computing with Qiskit; project helpout |
| 14 | Research codes and outlooks | Project helpout |
| 15 | Project presentations | (project 20pts) |
The different topics are scheduled to be covered in the following days.
| Week of | Monday | Tuesday (Session 1) |
Wednesday | Thursday (Session 2) |
Friday |
|---|---|---|---|---|---|
| Jan 9 | #1 | ||||
| Jan 16 | MLK Day | #1 | #2 | ||
| Jan 23 | #2 | #3 | |||
| Jan 30 | #3 | HW #3 due | #4 | ||
| Feb 6 | HW #3 due | #4 | HW #4 due | #5 | |
| Feb 13 | HW #4 due | #5 | HW #5 due | #6: project planning | |
| Feb 20 | HW #5 due | #6: project planning | HW #6 due | #7: project helpout | |
| Feb 27 | HW #6 due | #7: project helpout | #8: project presentations | ||
| Mar 6 | Spring break | ||||
| Mar 13 | #8: project presentations | HW #8 due | #9 | ||
| Mar 20 | HW #8 due | #9 | HW #9 due | #10 | |
| Mar 27 | HW #9 due | #10 | HW #10 due | #11 | |
| Apr 3 | HW #10 due | #11 | #12: project planning | ||
| Apr 10 | #12: project planning | HW #12 due | #13: project helpout | ||
| Apr 17 | HW #12 due | #13: project helpout | #14: project helpout | ||
| Apr 24 | #14: project helpout | Project due | #15: project presentations | ||
| Mar 1 | Project due | #15: project presentations | Last day of classes |
Chi-kwan "CK" Chan is a computational astrophysicist working with cutting edge technologies to advance both theoretical and observational research. He has developed new algorithms to study magnetohydrodynamic turbulence, used graphics processing units (GPUs) to accelerate general relativistic ray tracing, designed cloud computing infrastructures to handle big observational data, and applied machine learning algorithms to speed up and automate data processing. Some of CK's active projects include simulating and understanding accretion disks, capturing images of black holes, and visualizing astrophysical simulations in virtual reality. A true wildcat, CK received his bachelors and doctoral degrees from the University of Arizona. He is also a Data Science Fellow at the UArizona Data Science Institute.
Gabriele Bozzola is a graduate student specializing in computational and theoretical astrophysics. He uses large-scale simulations to study binary black hole mergers and their emissions. Gabriele is deeply interested in scientific software and a fervent advocate for open-source and open-science. He develops and maintains a few packages and modules for numerical-relativity simulations and contributes to open-source projects. Gabriele is currently a NASA future investigator for space science.
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Participating in the course and attending lectures and other course events are vital to the learning process. As such, attendance is required at all lectures and discussion section meetings. Absences may affect a student's final course grade. If you anticipate being absent, are unexpectedly absent, or are unable to participate in class online activities, please contact me as soon as possible. To request a disability-related accommodation to this attendance policy, please contact the Disability Resource Center at (520) 621-3268 or drc-info@email.arizona.edu. If you are experiencing unexpected barriers to your success in your courses, the Dean of Students Office is a central support resource for all students and may be helpful. The Dean of Students Office is located in the Robert L. Nugent Building, room 100, or call 520-621-7057.
Sessions will be recorded, therefore, students who register after the first class meeting may make up missed lectures and hands-on sessions by watching the recorded. Nevertheless, these students still need to finish the missing assignments and projects in a reasonable time frame. Note that the recordings should not be used as an excuse for the students to skip class (see above).
This course typically does not provide honors credit. For more information, please discuss directly with the instructor.
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