MAGIC098: Adaptive Finite Element Methods

Course details

Semester

Autumn 2021
Monday, October 4th to Friday, December 10th

Hours

Live lecture hours
10
Recorded lecture hours
0
Total advised study hours
40

Timetable

Tuesdays
14:05 - 14:55

Course forum

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Description

The main prerequisite is a strong motivation to undertake research related in modern aspects functional approximation theory, data compression, related algorithms, or the numerical analysis of partial differential equations.

A solid background in undergraduate analysis and partial differential equations, some basic functional or harmonic analysis, or numerical analysis will be useful. 

Prerequisites

Requirements are standard year 3 or master's level Analysis and some knowledge of elliptic partial differential equations.

Exposure to Galerkin or finite element methods (as taught in spring term MAGIC100 or equivalent) will be helpful though not essential. "Review" material will be uploaded. 

Syllabus

We start by reviewing the standard Galerkin method with a focus on numerical approximation methods such as wavelet Galekrin, finite elements and discontinuous Galerkin for elliptic and parabolic equations, including the needed element of functional analysis, e.g., Sobolev and Besov spaces.

We then recall the apriori error analysis of such methods and move onto aposteriori error analysis. We follow up this with an overview of the literature on adaptive methods and their convergence analysis with a focus on complexity of algorithms.

Time allowing we look at connections between wavelet and Galerkin methods or space-time methods for parabolic (perhaps hyperbolic) problems. (NB to be reduced to 10 hours) 

Lecturers

  • Dr Omar Lakkis

    Dr Omar Lakkis

    University
    University of Sussex
    Role
    Main contact
  • CV

    Dr Chandrasekhar Venkataraman

    University
    University of Sussex

Bibliography

No bibliography has been specified for this course.

Assessment

Attention needed

Assessment information will be available nearer the time.

Files

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Lectures

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