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Här visas ändringar i "Methods in Scientific Computing (DD2363), 7.5hp, Spring 2017" mellan 2017-01-04 00:15 av Johan Hoffman och 2017-01-04 13:42 av Johan Hoffman.
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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: Vector spaces
* Lecture 2: Linear transformations
* Lab 1
Week 2: Linear systems of equations
* Lecture 3: Linear systems of equations - Direct methods
* Lecture 4: Eigenvalue problems
* Lab 2
Week 3: Nonlinear systems of equations
* Lecture 5: Linear system of equations - Iterative methods
* Lecture 6: Nonlinear equations - Newton method
* Lab 3
Week 4: ODE
* Lecture 7: ODE - time stepping/quadrature in 1D
* Lecture 8: ODE models
* Lab 4
Week 4: Function approximation
* Lecture 9: Function approximation - piecewise polynomials, LS/L2-projection
* Lecture 10: Quadrature in 2D/3D - mesh, reference element, quadrature, assembly
* Lab 5
Week 6: PDE
* Lecture 11: PDE - model problem in 1D
* Lecture 12
* Lecture 12: PDE - model problem in 2D/3D
* Lab 6
Week 7: PDE
* Lecture 13: PDE models
* Lecture 14: Time dependent PDE
* Lab 7
Week 8: Optimization
* Lecture 15
* Lecture 16
* Lab 8
Week 9 Written exam