instructor and
contact
info: Ed Bueler Chapman 301C 4747693 elbueler@alaska.edu bueler.github.io textbook: Morton and Mayers, Numerical Solution of Partial Differential Equations, 2nd ed., Cambridge, 2005. 
time: MWF 1:002:00pm
room: Reichardt 204 crn: 34117 
This course covers numerical methods, and their analysis, for approximating partial differential equations (PDEs) and related problems on computers: How to do it in practice. How to determine how good a method is. How to choose among algorithms when facing a hard problem.
There will be many computed examples. Students will use Matlab/Octave to build algorithms and run them on concrete examples. There is an emphasis on thinking with matrices and vectors. We don't just list some finite difference schemes but try to think of them as matrix equations. (We will write small codes to build big matrices for computer solution.) I expose students to nonlinear examples because real problems are nonlinear. Students are encouraged to come with a particular problems in mind, and to do a class project on it.
Topics include:

Prerequisites: Officially, Prerequisites: CS F201, MATH F310, MATH F314, MATH F421, MATH F422 or permission of instructor.
Because you might be a graduate student with a background from another
University (!), I would translate this as: Exposure to the use of
computers to do
mathematics (a numerical course of
some kind like Math 310, ES 301, Phys 220, or at least
CS 201) plus undergraduate differential equations (Math 302) plus undergraduate
linear algebra (Math 314) plus some exposure to Fourier series and the method of
separation
of variables (Math 421 or
Math/Phys 611).