CSE Symposium Keynote
Gilbert Strang, Massachusetts Institute of Technology
TITLE:
Teaching and Learning Computational Science and Engineering
DATE: Wednesday, April 22, 2009
TIME: 9:00 A.M.
PLACE: 2240 DCL
1304 W. Springfield Ave., Urbana, IL
Abstract
I would like to discuss (together with the audience!) how we can go
forward with teaching computational science and engineering. It is a
fascinating and creative subject that combines applied mathematics with
scientific computing. How can we present both of those essential
parts? The need to move beyond older courses in engineering
mathematics is widely recognized. But a pure software course misses
the foundations for understanding new problems. Combining analysis
with computational science and engineering is truly powerful.
I will describe a course we have developed at MIT to address these
issues. My goal in this course is for each lecture to discuss a model
problem and a code to solve it. This course is popular with
engineering students and their departments, who want exposure to ideas
and also to software.
The main sections of the course are applied linear algebra (notice this
beginning!), differential equations, finite differences and finite
elements, Fourier methods, analytical methods, large sparse systems,
and optimization. The starting point is to understand the second
difference matrices (entries −1, 2, −1) that appear everywhere in
scientific computing and simulation.