0.0.1 Winter Semester 2019/2020

Scientific Computing (Wissenschaftliches Rechnen), TU Berlin

0.0.1.1 Exams

• The remaining exams will take place end of June/beginning of July in seminar room MA376
• Video exams are possible upon request

0.0.1.2 Content Summary

• Numerical methods focusing on partial differential equations (finite elements, finite volumes, mesh generation, linear and nonlinear solvers, iterative methods), their implementation and programming
• Julia programming language (as opposed to C++ in previous courses)
• Parallelization, visualization, software tools

0.0.1.3 Homework

0.0.1.4 Lecture material

The notebooks (.ipynb) files are meant for execution on Jupyter servers like that of the TU Berlin Unix pool. It can be accessed via your wir-xyz account of the UNIX pool. The uploaded notebooks have not been executed. The html and pdf versions are based on the executed variants.

A pdf combining the slides (without the recapitulations at the beginning) and parts of the pdf notebooks. is available:

0.0.1.5 Literature/Links

• Numerical methods:
  • G. Golub, C. van Loan Matrix Computations
  • Y. Saad: Iterative methods for sparse linear systems
  • R. S. Varga: Matrix Iterative Analysis
  • J. Shewchuk An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
  • R. Barrett et al: Templates for the Solution of Linear Systems:Building Blocks for Iterative Methods
  • V. Eijkhout: Introduction to High-Performance Scientific Computing
  • G. Bärwolff, Script, Numerische Mathematik I (TU Berlin, in German)
  • G. Bärwolff, Script, Numerische Mathematik II (TU Berlin, in German)
• Mesh generation:
  • J.R. Shewchuk: Lecture Notes on Delaunay Mesh Generation
  • Hang Si: Course material from 2019 International Summer School in Beihang University
• Julia:
  • Running/Installing/Editing:
    • TU Berlin Jupyter pool
    • Julia Pro: Curated Julia distribution, free download
    • Homepage: Download of the actual version
    • Julia via package managers: Homebrew, OpenSUSE, Ubuntu
    • Atom extension: Juno
    • Visual Studio Code extension
    • Emacs support
  • Learning:
    • Learning resources
    • Documentation
    • Cheat Sheet
    • QuantEcon tutorial
    • Julia by Example
    • WikiBook
    • VMLS Book Julia companion many linear algebra oriented examples
    • Think Julia Julia based introduction to programming
    • Noteworthy differences from Matlab
    • Noteworthy differences from Python
    • Matlab-Julia-Python cheat sheet If you look for further online resources, please ensure that they are for Julia 1.0 and newer. This is best achieved by looking for material not older than 1 year.
• Git (distributed version control system)
  • Homepage
  • git - the simple guide
  • Tutorial
• Linux
  • A Cheat Sheet
  • TuxArena: Introduction to Linux Command Line for Beginners

0.0.1.6 Page updates

(besides slide uploads)