Scientific Computing I - Winter 08
- Term
- Winter 08
- Lecturer
- Dr. Michael Bader
- Time and Place
- Wednesday, t.b.a., Raum 02.07.023, Beginn: 23.10.2008
- Audience
- Computational Science and Engineering, 1. Semester
- Tutorials
- -
- Exam
- written exam (time and day t.b.a.)
- Semesterwochenstunden / ECTS Credits
- 2 SWS / 3 Credits
- TUMonline
- {{{tumonline}}}
Contents
This course provides an overview of scientific computing, i. e. of the different tasks to be tackled on the way towards powerful numerical simulations. The entire "pipeline" of simulation is discussed:
- mathematical models: derivation, analysis, and classification
- numerical treatment of these models: discretization of (partial) differential systems, grid generation
- efficient implementation of numerical algorithms: implementation on monoprocessors vs. parallel computers (architectural features, parallel programming, load distribution, parallel numerical algorithms)
- interpretation of numerical results & visualization
- validation
The course is conceived as an introduction to the thriving field of numerical simulation for computer scientists, mathematicians, engineers, or natural scientists without an already strong background in numerical methods.
Lecture Notes and Material
(Material for future lectures refer to the lectures from winter term 2007, and will be updated throughout the semester)
- Introduction - Scientific Computing as a Discipline
- Oct
- slides, handout
- Fibonacci's Rabbits, Classification of Models
- Oct
- slides, handout
- Continous Population Models I - Single Species Models
- Nov
- slides
- Maple worksheet: popmodel.mws
- Continous Population Models II & III - Systems of ODE, Analysis of ODE Models
- slides, handout population models
- Maple worksheets: lotkavolt.mws, dirfields.mws
- Numerical Methods for ODE
- Nov
- slides, handout
- Maple worksheet: numerics_ode.mws
- Discrete Models for the Heat Equation
- Dec
- slides, handout
- Maple worksheet: poisson2D.mws
- Heat Equation - Analytical and Numerical Solution
- Dec
- slides, handout
- Maple worksheets: Fourier's method: heat1D_four.mws, Discretisation: heat1D_disc.mws, heat1D_impl.mws
- Additional material: Neumann stability (worksheet with solution), discrete energy (handout)
Exam
A written exam will be offered at the end of the lecture period.
Literature
- A.B. Shiflet and G.W. Shiflet: Introduction to Computational Science, Princeton University Press
- Boyce, DiPrima: Elementary Differential Equations and Boundary Value Problems, Wiley, 1992 (5th edition)
- Golub, Ortega: Scientific Computing: An Introduction with Parallel Computing, Academic Press, 1993
- Tveito, Winther: Introduction to Partial Differential Equations - A Computational Approach, Springer, 1998
- Stoer, Bulirsch: Introduction to Numerical Analysis, Springer, 1996
- Hackbusch: Elliptic Differential Equations - Theory and Numerical Treatment, Springer, 1992