Physics 212, 2020: Computational Modeling For Scientists And Engineers
Back to the main Teaching page.
- Welcome to the class!
- New sections are being added to the syllabus to account for the virtual learning we are switching to due to COVID-19.
About the class
Computation is one of the pillars of modern science, in addition to experiment and theory. In this course, various computational modeling methods will be introduced to study specific examples derived from physical, biological, chemical, and social systems. We will study how one makes a model, implements it in computer code, and learns from it. We will focus on modeling deterministic dynamics, dynamics with randomness, on comparison of mathematical models to data, and, at the end, on high performance computing. Students will learn Python programming language and will work on computational modeling projects in groups.
There are three goals that I have for students in the class:
- To learn to translate a descriptive formulation of a scientific problem into a mathematical / computational model.
- To learn how to solve such models using computers, and specifically using the Python programming language. This includes learning how to verify that the solution you produced is a correct solution.
- To learn basic algorithms used in computational science.
In addition, a minor goal of the class is to improve the students' ability to communicate their process of thinking and their results to others. To this extent, the class will require writing project reports, which will be graded on their clarity and completeness.
- Class Hours: M, W 10:00-11:15; MSC N 304
- Labs: Thu or Fri 2:30-5:30; MSC N303
- Office Hours
- Professor: Ilya Nemenman -- Monday and Thursday 12:00-1:00 (subject to change), and by appointment, MSC N240 or N117A if too many people.
- TA: Qihan Liu (Thursday lab), Office hour Monday 1:00-2:00 , MSC N117E
- TA: Emma Dawson (Friday lab), Office hour Wednesday 2:30-3:30, N209
- Syllabus -- I will try to keep close to the syllabus in the course of the semester, but some deviations are possible.
- Anaconda Python distribution (install Python v 3.X)
- Main Textbook: J Kinder and P Nelson, Student Guide to Python for Physical Modeling, 2nd edition, http://press.princeton.edu/titles/10644.html . This is the only textbook you should have; all others are optional.
- This tutorial is not a complete textbook. I will post additional lecture notes online as needed, or will direct you to additional chapters in other textbooks.
- See also Computational Modeling and Visualization of Physical Systems with Python by J Wang and Computational Physics by Giordano and Nakanishi.
- The bible of scientific computing is Numerical Recipies by Press et al.
- At the end of each class where we do coding, please submit your work using a Coding Snippet assignment submission on Canvas.
Lecture Notes and Detailed Schedule
- Class schedule is available in the syllabus.
- Below I will post Python notebooks for this class. I will strive to post changes to these notebooks before classes, but no promises.
- The Notebooks will also have project assignments for you to work on.
All of the notebooks we will use in the class are available from the Lecture Textbook repository. Currently the following notebooks are available:
- Chapter 1, Introduction to Computational Modeling; this is finalized, and is unlikely to change a lot.
- Chapter 2, Learning Python and solving algebraic equations; this is finalized, and is unlikely to change a lot.
- Chapter 3, Building and Solving Dynamical Models, this notebook is still being edited.
- Chapter 4, Optimization, this notebook is still being edited.
- Module 1, Progress Report 1 notebook, which covers the Introduction, and Chapters 1 and 2 of the Student Guide; this notebook is now finalized. You will need to (re)-submit this notebook on Jan 27th.
- Module 1, Progress Report 2 notebook, which covers Module 1 (Algebraic equations), and Chapters 3 and part of 4 of the Student Guide. The notebook is now finalized, and you need to submit it on Feb 3.
- Module 2, Progress Report 1, notebook, is not available; just do Your Turn exercises from the Module 2 notebook, up to RK2 algorithm.
- Module 3, Progress Report 1 notebook, which covers Module 3 up to and including the nonlinear 1-d optimization lecture (02/26). Submit this notebook on March 2.
- Labs 1, Jan 16-17
- Instal Anaconda.
- Do all exercises in the Module 1, Progress Report 1 notebook from the Lecture Textbook repository. This includes Your Turn questions from class, and exercises from Chapter 1 and Chapter 2 of the Student Guide. Finalized version of this notebook would need to be submitted on Jan 27.
- Chapters 1 and 2 and Appendix B of the Python Student Guide.
Module 1: Learning Python and solving algebraic equations
- Labs 2, Jan 23-24
- Do all exercises in the updated version of the Module 1, Progress Report 1 notebook from the Lecture Textbook repository. Submit or re-submit this updated and complete notebook on Jan 27.
- Do all exercises in the Module 1, Progress Report 2 notebook from the Lecture Textbook repository. This includes Your Turn questions from class to date, and exercises from Chapter 3 of the Student Guide. Do not submit this notebook on Jan 27th, and updated version will be due Feb 3.
- Chapters 3 of the Python Student Guide.
- Labs 3, Jan 30-31
- Do all exercises in the Module 1, Progress Report 2 notebook from the Lecture Textbook repository. This includes Your Turn questions from class to date, and exercises from Chapter 3 and some of Chapter 4 of the Student Guide. Submit the progress report by Feb 3.
- Sections 4.1 and 4.2 and Appendix E of the Python Student Guide.
- Labs 4, Jan Feb 6-7
- Do the project for Module 1 and submit on Monday.
Module 2: Dynamical models: Building and solving dynamical models
- Labs 5, Feb 13-14
- Do the 'Your Turn' exercises in the notebook up to (not including) RK2 and submit on Feb 17.
- See reading assignment in the Chapter 3 notebook above.
- Labs 6, Jan Feb 20-21
- You are not required to do the new Your Turn questions (3.8 - 3.18); these won't be submitted since we have only one Progress Report for this module, not two. However, I strongly recommend that you try to do some of them in your spare time.
- Do the project for Module 2 and submit on Monday 2/24.
Module 3: Optimization
- Labs 7, Jan Feb 27-28
- Do all exercises in the Module 3, Progress Report 1 notebook from the Lecture Textbook repository. Submit the progress report by March 2.