MAGIC098: Adaptive Finite Element Methods

Course details


Autumn 2020
Monday, October 5th to Friday, December 11th


Live lecture hours
Recorded lecture hours
Total advised study hours


10:05 - 10:55


Course material is found (and constantly updated) on this online repository:

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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. 


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. 


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) 


  • Dr Omar Lakkis

    Dr Omar Lakkis

    University of Sussex
    Main contact
  • CV

    Dr Chandrasekhar Venkataraman

    University of Sussex


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Assessment is due in January (rely on MAGIC admin for precise dates) and appears here over the winter break. It consists of one long question with a menu including analysis and computations, which can be self-tailored by the student.

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