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SDS 401: Mathematical Modeling and Algorithms

 

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 SDS 401: Mathematical Modeling and Algorithms is a 10 ECTS mandatory course for the Simulation and Data Science Master's program. The course does not have prerequisites.  All courses are offered in English.


Course Instructor

The course instructor is Dr. Yury Suleymanov


Course Purpose

The course’s purpose is to introduce mathematical tools and algorithms used in computational sciences focusing on methods used in numerical simulation and data analysis. 



Course Content 

Linear algebra revew, numerical differentiation and integration of funcitons, direct methods for linear system, nonlinear sets of equations, polynomial interpolation and extrapolation, sorting algorithms, fast Fourier transform.  Introduction to the theory of partial differential equations (PDEs) and methods of solving linear and non-linear PDEs. Students will also learn how to solve equations that come from the world of physics and other sciences. 


Learning Objectives

By the end of the course students will be equipped with core techniques behind efficient methods to find numerical solutions to differential equations, iterative techniques to solve large, sparse linear systems, introduce modelling of real systems via partial differential equations and how to solve them numerically using examples from physical science and engineering domains. 



Teaching Methodology

Lectures, exercises where numerical recipies will be examined.

 

Assessment

25% coursework, 75% exam



Bibliography

Uri Ascher and Chen Greif, “A First Course in Numerical Methods”, SIAM, 2011

H.Gould, J. Tobochnik and W. Christian, “An introduction to Computer Simulation Methods: Applications to Physical Systems”



 

 

 

 

 

 

 

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