|Time/place||Wed/Fri 11:00am-12:15pm 1304 Siebel / Catalog|
|Live lecture||Participate » (requires password) · Backup: Twitch Stream|
|Class recordings||Watch » · Alternate, prior to spring break: Echo 360|
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This course covers the basics of finite difference schemes, finite volume schemes, and finite element methods (majority). In addition, we'll cover some advanced topics such as discontinuous Galerkin and spectral methods.
One of the goals of this course is to build intuition for these methods. We'll be using Python and will be providing background for many of the computational and mathematical concepts in the course. As such, you do not need to be an expert in PDEs or in coding. But you should have a course in numerical analysis as your background (CS450 or equivalent), be comfortable with differential equations, and have some coding experience.
The course is divided in roughly two parts: hyperbolic and elliptic. This is of course a generalization, but it does allow us to focus on finite difference/finite volume methods for one part of the course and finite elements for another part. In addition to model problems we'll look at Stokes and other equations in order to develop a full understanding of the methods.
The course involves several homeworks (usually bi-weekly) and two projects: a midsemester project and a final project. There is also a participation grade based on quizzes.
The course homeworks and examples in class will be in Python. In particular, we'll use numpy and scipy.
Office: 3401 Siebel
Note: the section headings in this tree are clickable to reveal more detail.
These scribbled PDFs are an unedited reflection of what we wrote during class. They need to be viewed in the context of the class discussion that led to them. See the lecture videos for that.
If you would like actual, self-contained class notes, look in the outline above.
These scribbles are provided here to provide a record of our class discussion, to be used in perhaps the following ways:
By continuing to read them, you acknowledge that these files are provided as supplementary material on an as-is basis.
We will be using Python with the libraries numpy, scipy and matplotlib for assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.
While you are free to install Python and Numpy on your own computer to do homework, the only supported way to do so is using the supplied virtual machine image.
Once you sign in and complete your enrollment in RELATE, you will gain access to a draft textbook that was made available by Luke Olson.