Difference between revisions of "Scientific Computing II  Summer 13"
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 June 4  [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss13/conjugate.pdf CG and Preconditioning (cont.)]   June 4  [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss13/conjugate.pdf CG and Preconditioning (cont.)]  
−   June  +   June 10  <! [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss12/tutorial_04/04_steepest_descent_cg_tutorial.pdf SteepestDescent/CG] <br> [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss12/tutorial_04/04_tutorial_krylov.pdf Slides] >  <! [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss12/tutorial_04/framesd.tar.gz SD/CG] [http://www5.in.tum.de/lehre/vorlesungen/sci_compII/ss12/tutorial_04/SOLUTION_SD.tar.gz SolutionSD] > 
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Revision as of 15:10, 3 June 2013
 Term
 Summer 13
 Lecturer
 Prof. Dr. Michael Bader
 Time and Place
 Tuesday 1012, lecture room MI 02.07.023
First Lecture: Apr 16  Audience
 Computational Science and Engineering, 2nd semester
 Tutorials
 Wolfgang Eckhardt Philipp Neumann
Monday 1012, lecture room MI 02.07.023,
First Tutorial: April 22  Exam
 written exam
 Semesterwochenstunden / ECTS Credits
 2V + 2Ü / 5 Credits
 TUMonline
 Scientific Computing II
Contents
Announcements
 due to the student assembly, the lecture on Tuesday, May 14, is skipped
 due to a short holiday (Whit Monday/Pentecost), lecture and tutorial on May 20/21 will be skipped
 on Mon 27, we will restart with a lecture (which replaces the usual tutorial)
Exam
 written exam
Contents
This course provides a deeper knowledge in two important fields of scientific computing:
 iterative solution of large sparse systems of linear equations:
 relaxation methods
 multigrid methods
 steepest descent
 conjugate gradient methods
 molecular dynamics simulations
 the physical model
 the mathematical model
 approximations and discretization
 implementational aspects
 parallelisation
 examples of nanofluidic simulations
The course is conceived for computer scientists, mathematicians, engineers, or natural scientists with already a background in the numerical treatment of (partial) differential equations.
Lecture Notes and Material
lecture  material  tutorial  exercise  matlab 
Apr 16  Introduction, Relaxation Methods  Apr 22  Sheet1, Solution  
Apr 23  Multigrid Methods, Animations  Apr 29  Sheet2, Solution, smoothers.m  
Apr 30  Multigrid Methods (Part II)  May 06  Sheet3, Solution, code_exercise3.tar  
May 07  Multigrid Methods (Part III)  May 13  Sheet4, smooth.m, Solution, code_exercise4.tar  
Mai 14  (student assembly  no lecture)  May 20  (holiday  no lecture)  
Mai 21  (holiday  no lecture)  May 27  Steepest Descent and Conjugate Gradient Methods (Maple worksheet quadratic_forms.mws, also as PDF) 

May 28  CG and Preconditioning (Maple worksheet conjugate_gradient.mws, also as PDF) 
June 3  Sheet5, Solution  
June 4  CG and Preconditioning (cont.)  June 10 
Literature
 William L. Briggs, Van Emden Henson, Steve F. McCormick. A Multigrid Tutorial. Second Edition. SIAM. 2000.
 Ulrich Trottenberg, Cornelis Oosterlee, Anton Schüller. Multigrid. Elsevier, 2001.
 J.R. Shewchuk. An Introduction to the Conjugate Gradient Method Without the Agonizing Pain (download as PDF). 1994.
 M. Griebel, S. Knapek, G. Zumbusch, and A. Caglar. Numerische Simulation in der Molekulardynamik. Springer, 2004.
 M. P. Allen and D. J. Tildesley. Computer Simulation of Liquids. Oxford University Press, 2003.
 D. Frenkel and B. Smith. Understanding Molecular Simulation from Algorithms to ASpplications. Academic Press (2nd ed.), 2002.
 R. J. Sadus. Molecular Simulation of Fluids; Theory, Algorithms and ObjectOrientation. Elsevier, 1999.
 D. Rapaport. The art of molecular dynamics simulation. Camebridge University Press, 1995.
Further Material
Annotated slides for the lecture in summer 2010 /(given by Dr. Tobias Weinzierl) are available from the TeleTeachingTool Lecture Archive
Matlab (together with installation instructions) is available from https://matlab.rbg.tum.de/