Department of Mathematics

Numerical Methods for Partial Differential Equations

Please note that this page is old.
Check in the VVZ for a current information.

Ferienpräsenz: we offer consultation hours during the semester break where you can ask question about the lecture material. For the dates see  this document (in German). It also lists the dates for the Prüfungseinsicht.

Lecturer: Siddhartha Mishra

Assistants: Jonas Sukys, Laura Scarabosio, Simon Etter

Lecture hours:

Exercise classes:

For INFK students, please choose an exercise class here.
For CSE students, please attend your dedicated tutorial class.

Consultation hours:

Consultation hours will be announced in the first exercise class.


There will be assignment sheets every two weeks, handed out every Monday (first time on February 24th).

You can hand in your solutions either during the exercise class or in the labeled box at HG G 53.X.
Correction of homework problems will be done on request.
Remark: in general, the codes will not be corrected, but you can approach us with a specific question.

Testat requirements: NONE.

Bonus points: from the second assignment on, each assignment sheet will contain a problem marked as Core problem. The core problems will be corrected and full mark for them will give a 20% bonus of the total points in the final exam.

For submitting your code, please use the online submission interface and choose the course n....

Assignments, lecture notes and other handouts


Info will be given during the first lecture.

At least one of the problems presented in the assignment sheets will be an exam problem.

Aims of the course

Introduce students of Applied Mathematics, Computational Science and Engineering and Computer Science to the most widely used numerical solution methods for ordinary and partial differential equations, their mathematical properties, and their computer implementation.

Students should be able to:

Programming Language: implementations can be done in MATLAB, Python, C or C++.

Content of the course

See a more comprehensive list on the VVZ.

Matlab / Python links

Students of ETH can download Matlab via Stud-IDES for free (product name 'Matlab free').

Here is an introduction to Python: Python introduction.


This list should be interpreted as supplementary reading beyond the lecture notes, and is neither important nor required for following the course.

Further literature

These are beyond the scope of this course, and are listed here for the particularly interested.


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© 2016 Mathematics Department | Imprint | Disclaimer | 24 March 2014