Scientific computing

linma2710  2019-2020  Louvain-la-Neuve

Scientific computing
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Absil Pierre-Antoine (coordinator); Meerbergen Karl (compensates Papavasiliou Anthony); Papavasiliou Anthony;
Language
English
Prerequisites
Basic training in numerical methods and programming (level of LFSAB1104).
Main themes
  • Numerical software in C++ and Python
  • Parallel computing
  • Numerical methods for partial differential equations
Aims

At the end of this learning unit, the student is able to :

1 Contribution of the course to the program objectives (Nr) :
  • AA1.1, AA1.2, AA1.3
  • AA2.2, AA2.3, AA2.4
  • AA3.2
  • AA6.1, AA6.3
After successful completion of this course, the student will be able to:
  • Write, modify and use numerical software in C++ and Python;
  • Write, modify and use scientific software for partial differential equations;
  • Employ parallel programming techniques
Transversal learning outcomes :
  • Use a reference book in English;
  • Use programming languages for scientific computing;
  • Release software along with suitable user documentation.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
  • Programming concepts in C++ and Python
  • Numerical software engineering in C++ and Python
  • Analysis of partial differential equations
  • Numerical methods for partial differential equations
  • Introduction to parallel computing using MPI
  • Other topics related to the course themes.
Teaching methods
  • Interactive lectures
  • Homework assignments, exercises, or laboratory work under the supervision of the teaching assistants
Evaluation methods
  • Homeworks, exercises, or laboratory work during the course semester
  • Exam
Clarifications are provided in the course outline (plan de cours) available on Moodle.
Other information
The organisation details are given every year in the course outline.
Bibliography
  • Ouvrages de référence
  • Documents complémentaires disponibles sur Moodle
Des précisions sont fournies dans le plan de cours disponible sur Moodle.
Faculty or entity
MAP


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Computer Science and Engineering

Master [120] in Mathematical Engineering

Master [120] in Computer Science