Scientific Computing
Methoden des Scientific Computing
8 CPirregularlyEN
Prerequisites
Recommended, as written
Knowledge of the content of the modules: • 11925 Grundlagen der Numerischen Mathematik • 11943 Grundlagen des Wissenschaftlichen Rechnens • 11414 Funktionentheorie und Partielle Differentialgleichungen as well as programming skills, typically Matlab and C / Fortran
Contents
This module aims to teach advanced methods of scientific computing. The methodological focus lies on advanced computational techniques in numerical linear algebra and related fields. Topics include: a selection of advanced methods for computing eigenvalues and eigenvectors, singular value decomposition, numerical aspects of least-squares approximation, and a selection of matrix factorization methods.
Learning outcomes
Upon successful completion of the module, students will have acquired advanced knowledge for understanding modern numerical methods across a wide range of fields in science and engineering.
Assessment
• Three written tests, 30 minutes each (each 30%) • Presentation of the practical training, 15 minutes (10%)
Literature (1)
- The literature in use may change over time and will be announced at the first class meeting.
Remarks
• Study programme Angewandte Mathematik M.Sc.: Compulsory elective module in complex „Numerics“ • Study programme Mathematik B.Sc.: Compulsory elective module in complex „Specialisation“, in limited extend • Study programme Wirtschaftsmathematik B.Sc.: Compulsory elective module in complex „Specialisation“, in limited extend • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“ • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Advanded Methods“ • Study programme Mathematics M.Sc.: Compulsory elective module in complex „Numerics“ • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Advanced Mathematical Methods in Data Science“
Source: BTU's module description · retrieved 29 Sep 2026Original