Scientific Computing II - Summer 13: Difference between revisions

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| term = Summer 13
| term = Summer 13
| lecturer = [[Michael Bader|Prof. Dr. Michael Bader]]
| lecturer = [[Michael Bader|Prof. Dr. Michael Bader]]
| timeplace = Tuesday 8:30-10:00, lecture room MI 02.07.023<br/>First Lecture: T.B.A.
| timeplace = Tuesday 10-12, lecture room MI 02.07.023<br/>First Lecture: Apr 16
| credits = 2V + 2&Uuml; / 5 Credits
| credits = 2V + 2&Uuml; / 5 Credits
| audience = Computational Science and Engineering, 2nd semester (Module [https://www.in.tum.de/myintum/kurs_verwaltung/cm.html?id=IN2141 IN2141])
| audience = Computational Science and Engineering, 2nd semester (Module [https://www.in.tum.de/myintum/kurs_verwaltung/cm.html?id=IN2141 IN2141])
| tutorials = [[Wolfgang Eckhardt]]  [[Philipp Neumann]] <br> lecture room MI 02.07.023, time: <br/> Monday 9:00-10:30, <br/> First Tutorial: T.B.A.| exam = written exam  
| tutorials = [[Wolfgang Eckhardt]]  [[Philipp Neumann]] <br> lecture room MI 02.07.023, time: <br/> Monday 10:00-12, <br/> First Tutorial: April 22
| exam = written exam  
| tumonline = [https://campus.tum.de/tumonline/LV.edit?clvnr=950099284 Scientific Computing II]
| tumonline = [https://campus.tum.de/tumonline/LV.edit?clvnr=950099284 Scientific Computing II]
}}
}}

Revision as of 16:35, 13 March 2013

Term
Summer 13
Lecturer
Prof. Dr. Michael Bader
Time and Place
Tuesday 10-12, lecture room MI 02.07.023
First Lecture: Apr 16
Audience
Computational Science and Engineering, 2nd semester (Module IN2141)
Tutorials
Wolfgang Eckhardt Philipp Neumann
lecture room MI 02.07.023, time:
Monday 10:00-12,
First Tutorial: April 22
Exam
written exam
Semesterwochenstunden / ECTS Credits
2V + 2Ü / 5 Credits
TUMonline
Scientific Computing II



Announcements

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

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/

Literature

  • William L. Briggs, Van Emden Henson, Steve F. McCormick. A Multigrid Tutorial. Second Edition. SIAM. 2000.
  • 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 Object-Orientation. Elsevier, 1999.
  • D. Rapaport. The art of molecular dynamics simulation. Camebridge University Press, 1995.
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