Scientific Computing Lab - Winter 15: Difference between revisions

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= Announcements =
= Announcements =
<!--* For the schedule and all other information, see [https://www.moodle.tum.de/course/view.php?id=15388 the moodle page].-->
* For all course contents (slides, worksheets, submission of code, choice of examination slots), see [https://www.moodle.tum.de/course/view.php?id=22950 the moodle page]


= Contents =  
= Contents =  
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* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/mprimer.pdf Slides]
* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/mprimer.pdf Slides]
* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/exercises_matlab_day1.pdf Worksheet 1]
* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/exercises_matlab_day1.pdf Worksheet 1]
|* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/exercises_matlab_day2.pdf Worksheet 2]
* [http://www5.in.tum.de/lehre/praktika/scicomp/ws15/matlab/exercises_matlab_day2.pdf Worksheet 2]
 
<!--{| class="wikitable"
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| <b>Slides</b> || <b>Tutorials</b>
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| [http://www5.in.tum.de/lehre/praktika/scicomp/ws13/matlab/mprimer.pdf Slides] || [http://www5.in.tum.de/lehre/praktika/scicomp/ws13/matlab/exercises_matlab_day1.pdf Worksheet 1]
[http://www5.in.tum.de/lehre/praktika/scicomp/ws13/matlab/exercises_matlab_day2.pdf Worksheet 2]
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= Literature =
= Literature =

Latest revision as of 17:00, 21 October 2015

Term
Winter 15
Lecturer
Dr. rer. nat. Tobias Neckel,
Emily Mo-Hellenbrand, M.Sc., Dipl.-Math. Benjamin Uekermann
Time and Place
see TUMonline or moodle
Audience
Students of Computational Science and Engineering, compulsory course, first semester,
Tutorials
-
Exam
no final exam
Semesterwochenstunden / ECTS Credits
4 SWS (4P) / 6 credits
TUMonline



Announcements

  • For all course contents (slides, worksheets, submission of code, choice of examination slots), see the moodle page

Contents

The lab course gives an application oriented introduction to the following topics:

  • explicit and implicit time stepping methods for ordinary differential equations
  • numerical methods for stationary and instationary partial differential equations
  • solvers for large, sparse systems of linear equations
  • adaptivity and adaptively refined discretisation grids
  • applications from fluid dynamics and heat transfer

Basics in linear algebra and differential calculus are required.

Introduction to Matlab

Literature

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