Lehrinhalte
Scientific computing is introduced via six case studies. Exemplary engineering problems that are know from basic engineering courses are solved on a computer using fundamental methods from numerical mathematics. Opportunities and limitations of this approach are highlighted.
The required material on numerical mathematics is taught via preparatory scripts for each case study. During the practical exercises the methods are implemented in the current computing environment Python under the guidance of suitable teaching personnel.
The case studies cover the following numerical topics:
[list]
[*]Formulation and solution of systems of linear equations, sparse methods
[*]Integration of ordinary differential equations (ODE) and their analysis based on eigenvalues
[*]Mathematical optimization and automated differentiation
[*]Linear regression and approximation, first Machine Learning algorithms
[*]Discretization of simple partial differential equations (PDE)
[/list]
Voraussetzungen
Etit 1 & 2, Mathe für etit 1-3
Scientific computing is introduced via six case studies. Exemplary engineering problems that are know from basic engineering courses are solved on a computer using fundamental methods from numerical mathematics. Opportunities and limitations of this approach are highlighted.
The required material on numerical mathematics is taught via preparatory scripts for each case study. During the practical exercises the methods are implemented in the current computing environment Python under the guidance of suitable teaching personnel.
The case studies cover the following numerical topics:
[list]
[*]Formulation and solution of systems of linear equations, sparse methods
[*]Integration of ordinary differential equations (ODE) and their analysis based on eigenvalues
[*]Mathematical optimization and automated differentiation
[*]Linear regression and approximation, first Machine Learning algorithms
[*]Discretization of simple partial differential equations (PDE)
[/list]
Voraussetzungen
Etit 1 & 2, Mathe für etit 1-3
- Lehrende: Herbert De Gersem
- Lehrende: Heinz Köppl
- Lehrende: Markus Meinert
- Lehrende: Sebastian Schöps
- Lehrende: Florian Steinke
Semester: WT 2020/21