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¶