Instructor: Ari Stern

Email: astern@math.wustl.edu

Office: Cupples I, 211B

Office Hours: TuTh 11am-12pm

Problem sets will be posted approximately every 1-2 weeks, and
will be collected at the beginning of class on the due date. You
are encouraged to discuss the homework with your fellow students
and to collaborate on problems, but
*your final write-up must be your own*. Please make sure that your
solutions are written clearly and legibly.

Melissa Zhang (melissa.zhang@wustl.edu) is responsible for grading the homework assignments.

- HW1: handout [pdf], code [py]. Due Friday, January 23.
- HW2: handout [pdf], code [py]. Due Friday, February 6.
- HW3: handout [pdf], code [py]. Due Friday, February 20.
- HW4: handout [pdf], code [py]. Due
**Wednesday**, March 18. - HW5: handout [pdf]. Due Wednesday, April 1.
- HW6: handout [pdf], code [py]. Due Friday, April 17.

Lectures will be held MWF 3-4pm, in Cupples I, 113. The first class will be on Monday, January 12, and the last will be on Friday, April 24. Class will be canceled for Martin Luther King, Jr. Day (Monday, January 19) and for Spring Break (week of March 8-14).

The midterm exam was held in class on Wednesday, March 4. The final exam is scheduled for Thursday, April 30, from 6-8pm.

Grades will be based on a weighted average of homework (40%, lowest score dropped), midterm exam (20%), and final exam (40%).

The text for this course is *A First Course in the Numerical
Analysis of Differential Equations*, by Arieh Iserles,
published by Cambridge University Press.

The programming component of this class is based on the Python programming language with the SciPy collection of numerical and scientific computing tools. This software is free and open source, and can be installed on your own computer.

Most students enrolled in the course will have previous experience with Python/SciPy from Math 449 last term. To students who did not take 449 last term, I recommend reviewing the programming assignments (and solutions), listed on the class web page, in order to get up to speed. You may also find it helpful to look at the the SciPy Getting Started guide.

The Anaconda Python Distribution is officially recommended for this course, and is available for Linux, Mac, and Windows.

Application and analysis of numerical methods for ordinary and partial differential equations. Specific topics may include: Runge-Kutta methods, geometric numerical integrators, finite difference methods, finite element methods, spectral methods, etc. Prerequisites: Math 449 or permission of instructor.