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The most diverse equations resulting from physical systems can not be explicitly solved in closed form, and thus numerical solutions are invaluable to obtain information about the underlying physical system. The first half of the module deals with ordinary differential equations. Several different numeric methods are introduced, and the growth of the error is examined. Both initial and limit problems are examined. The second half of the module deals with the numerical solution of partial differential equations. The curriculum contains: initial value problems for ordinary differential equations; Taylor methods; Runge-Kutta methods; Multist methods; Error limits and stability; Limit issues for ordinary differential equations; finite difference schemes; Differential systems for partial differential equations; iterative methods; Stability analysis.
Overall contact hours: 42
Private study times: 108
Total study period: 150
80% examination, 20% course work
Burden, RL and Faires, JD and Burden, Am, Numerical Analysis, 10th Edition, Centgage Learning, 2016
Iserles, a first course in the numerical analysis of differential equations, 2nd edition, Cambridge University Press, 2009
Morton, KW and Mayers, DF, numerical solution of partial differential equations: An introduction, Cambridge University Press, 2011
The intended topic-specific learning outcomes.
When successful completion of the module students:
1 will demonstrate systematic understanding of the most important aspects of the finite differential methods for approximating solutions from ordinary differential equations (ODES) and partial
differential equations (PDES);
2 Demonstrate the ability to accurately use established approaches to analyze and solve problems with a reasonable degree of skills in the calculation and manipulation of
the material in the following areas: MultisteP methods , Approaching limit issues for odes, discretization of PDES, error and stability analyzes, /> Elemental numerical linear algebra;
3 Apply the most important aspects of the finite differential methods in well-defined contexts, show the verdict in the selection and application of tools and techniques;
4 Show the verdict in the selection and application of MATLAB commands to implement numerical methods.
The intended generic learning outcomes.
When successful completing the module, students:
1 manage their own learning and use suitable resources.
2 Understanding logical arguments, identify the assumptions taken and the conclusions;
3 Communicate uncomplicated arguments and conclusions reasonably accurately and clearly;
4 Manage your time and use your organizational skills to plan and implement efficient and effective work of work;
5 solve problems relating to qualitative and quantitative information;
6 Make a competent use of information technology skills such as online resources (Moodle), Internet communication;
7 Communicate technical material competent;
8 demonstrate an increased level of ability in terms of calculation and calculation;
9 demonstrate the acquisition of the study capabilities required for the further development of vocational development.
University of Kent makes every effort to ensure that module information is accurate for the appropriate academic session and offer as described as educational services. However, courses, services and other affairs can be subject to changes. Please read our full disclaimer.
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