You will learn the most common numerical methods used to solve complex physical, biological, financial or geological phenomena. Examples of methods are numerically derivation, numerical integration, Monte Carlo and boot strapping methods, inverse methods, numerical solution of common differential equations, simulated annealing, and colony optimization, lattice Boltzmann models, random walk models, box (compartment) models.
The primary programming language is Python. Through assignments, you will learn how to set up mathematical models of a phenomenon, develop algorithms, implement them, and investigate the strength and limitations of the solution method and the mathematical model.
- Advanced knowledge in the use of algorithms and computational thinking to solve discrete and continuous problems
- Understand the constraints associated with the chosen solution method, including approximation errors and constraints linked to the selection of specific algorithms or numerical methods.
- In depth knowledge of the basic numerical methods
- Develop models of physical systems from biology, chemistry, flow in porous media, and geology
- Test models against experimental data, and use data to constrain the model
- Apply computational thinking to solve mathematical models by the use of appropriate numerical methods
- Develop own programs written in the program language Python
- Visualize and presentation of results from numerical simulations
- The use of computers to work more efficiently with large amounts of data
Required prerequisite knowledge
Matematiske Metoder 1 (MAT100), Linær Algebra (MAT110), Differensialligninger (MAT320), Numerisk Modellering Grunnkurs (MAF300)
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Method of work
Supporting literature: "Computational Physics" M. Newman (http://www-personal.umich.edu/~mejn/cp/index.html )
Last updated: 10.12.2019