Physics 212, 2020: Computational Modeling For Scientists And Engineers
Back to the main Teaching page.
- Welcome to the class!
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
- TA: Emma Dawson (Friday lab), Office hour Wednesday 2:30-3:30, N302
- 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
- Module 1, Progress Report 1 notebook, which covers the Introduction, and Chapters 1 and 2 of the Student Guide
- 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.
- Chapters 1 and 2 and Appendix B of the Python Student Guide.
- Lecture 3, First steps in Python.
- Labs 2, Jan 23-24
- Chapter 3 of the Python Student Guide.
- Additional reading
- Numerical Recipes book, Chapter 9, talks about finding roots of nonlinear functions, including the Newton-Raphson method and, in Section 9.6, the multidimensional version of it. Those interested, should read the general considerations in Section 9.0, and then whichever additional sections you find interesting.