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Methods in Scientific Computing (DD2365), 7.5hp, Spring 2017
Course goals
The goal of the course is to present general and efficient numerical methods and algorithms for basic models of computational science, in particular particle models, ordinary differential equations (ODE) and partial differential equations (PDE). Research challenges in the field are highlighted, e.g. with respect to parallel and distributed computing.
Teachers
Johan Hoffman
Johan Jansson
Niclas Jansson
Literature
TBA
Lab modules
Lab 1: Vector spaces and linear transformations
Lab 2: Linear systems of equations
Lab 3: Nonlinear systems of equations
Lab 4: Function approximation
Lab 5: ODE
Lab 6: PDE I
Lab 7: PDE II
Lab 8: Optimization
Week plan
Week 1: Vector spaces and linear transformations
- Lecture 1
- Lecture 2
- Lab 1
Week 2: Linear systems of equations
- Lecture 3
- Lecture 4
- Lab 2
Week 3: Nonlinear systems of equations
- Lecture 5
- Lecture 6
- Lab 3
Week 4: Function approximation
- Lecture 7
- Lecture 8
- Lab 4
Week 5: ODE
- Lecture 9
- Lecture 10
- Lab 5
Week 6: PDE
- Lecture 11
- Lecture 12
- Lab 6
Week 7: PDE
- Lecture 13
- Lecture 14
- Lab 7
Week 8: Optimization
- Lecture 15
- Lecture 16
- Lab 8
Week 9
Written exam