Scientific Computing (Wissenschaftliches Rechnen)

TU Berlin, Winter Semester 2021/2022

Updates

New literature fitting the topics of the course quite well:

P. Knabner, L. Angermann: Numerical Methods for Elliptic and Parabolic Partial Differential Equations Available via TU Berlin subscription. If you have access problems, please contact me.

Mode of operation

Weekly lecture material will be made available during the week via videos, slides and programming notebooks.
Online Q&A meetings on Fridays 14:00 c.t. on Zulip chat as communication tool
Meeting in presence possible with up to 47 participants
- The current pandemic rules require:
  - "3G": Geimpft/Genesen/Getestet (vaccinated or recovered or tested negatively)
  - "Vaccinated" status needs a sticker for your "Studierendenausweis": https://www.tu.berlin/nachrichtendetails/ausgabe-impfsticker-studierendenausweis-und-auskleber-fuer-vorlaeufige-fahrtberechtigung/
  - Wear masks
  - 1.5 m distance. FFP2 masks mandatory if this cannot be ensured
  - I will translate this via zoom for those who cannot attend
- If this does not work out we will fall back to zoom meeting on Fridays 14:00 c.t.

Content summary

Numerical methods focusing on partial differential equations (finite volumes,finite elements, mesh generation, linear and nonlinear solvers, iterative methods) and their implementation
Julia programming language
Parallelization, visualization, research software best practices

Exams

Exams will be performed as "Portfolioprüfung" based on presentations of group projects of groups of three students. Different possible exam topics can be found here.

Literature/Links

Numerical methods:
- P. Knabner, L. Angermann: Numerical Methods for Elliptic and Parabolic Partial Differential Equations Available via TU Berlin subscription.
- Y. Saad: Iterative methods for sparse linear systems
- V. Eijkhout: Introduction to High-Performance Scientific Computing
- 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. Golub, C. van Loan: Matrix Computations
- 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:
  - Homepage: Download of the latest version from here
  - Visual Studio Code extension
  - Pluto notebooks; How to install Julia and Pluto: MIT course video
  - Emacs support
  - Workflow, performance etc
- Learning:
  - Learning resources
  - Documentation
  - Cheat Sheet
  - QuantEcon tutorial
  - Julia by Example
  - Julia for Talented Amateurs
  - WikiBook
  - VMLS Book Julia companion many linear algebra oriented examples
  - Think Julia Julia based introduction to programming
  - Noteworthy differences from Matlab
  - Noteworthy differences from R
  - Noteworthy differences from Python
  - Noteworthy differences from C/C++
  - 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 2019.
Git (distributed version control system)
Linux
- A Cheat Sheet
- TuxArena: Introduction to Linux Command Line for Beginners