Math 450
Numerical Methods for Differential Equations, Spring 2018
Basic Information
Instructor: Ari Stern
Email: stern@wustl.edu
Office: Cupples I, 211B
Office Hours: TuTh 1:30-3pm
Homework Assignments
Problem sets will be posted approximately biweekly, 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.
Lucy Huo (huodongyan@wustl.edu) is responsible for grading the homework assignments.
Access to homework solutions is restricted to enrolled students via WUSTL Key.
- HW1: handout [pdf], code [py]. Due Friday, January 26.
- HW2: handout [pdf], code [py]. Due Friday, February 9.
- HW3: handout [pdf], code [py]. Due Friday, March 2.
- HW4: handout [pdf], code [py]. Due Friday, March 30.
- HW5: handout [pdf]. Due Friday, April 13.
- HW6: handout [pdf], code [py]. Due Friday, April 27.
Lectures
Lectures will be held MWF 3-4pm, in Sever 102. The first class will be on Wednesday, January 17, and the last will be on Friday, April 27. Class will be canceled for Spring Break (week of March 11-17).
Exams
There will be one in-class midterm exam on Wednesday, March 7. The final exam will be held on Thursday, May 3, from 6-8pm, in Sever 102 (our usual classroom).
Grading
Grades will be based on a weighted average of homework (40%, lowest score dropped), midterm exam (20%), and final exam (40%).
Textbook
The text for this course is A First Course in the Numerical Analysis of Differential Equations, by Arieh Iserles, published by Cambridge University Press.
Software
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.
Academic Integrity
All students are expected to adhere to high standards of academic integrity, as specified in the undergraduate student academic integrity policy. Since this course is offered through the College of Arts & Sciences, any violations of this policy will be referred to the College’s Academic Integrity Officer. Violations of this policy include, but are not limited to:
- homework collaboration exceeding that permitted in the Homework Assignments section above (e.g., preparing your final write-up with another student, copying another student's solutions or code);
- submitting the work of others as your own (e.g., copying solutions or code found online);
- exam misconduct (e.g., copying another student's exam or knowingly allowing your exam to be copied, giving or receiving unauthorized assistance, use of unauthorized books/notes or other materials, unauthorized use of electronic devices, etc.).
If you have any questions, or are unsure about what is permitted/prohibited by this policy, please ask me.
In many cases, academic integrity violations are the result of getting behind in coursework and making bad decisions under pressure. Keep up with your assignments, ask questions when you are unsure what is expected of you, and do not give in to the temptation to cut corners.
Catalog Description
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.